It seems that no one has integrated this literature yet. I will take a quick stab at it here. It could be expanded into a proper paper later in case someone wants to and have time to do that.


Lee Jussim (also blog) has done a tremendous job at reviewing the stereotype in recently years. In general he has found that stereotypes are mostly moderately to very accurate. On the other hand, self-fulfilling prophecies are probably real but fairly limited (e.g. work best when teachers don’t know their students well yet), especially in comparison to stereotype accuracy. Of course, these findings are exactly the opposite of what social psychologists, taken as a group, have been telling us for years.

The best short review of the literature is their book chapter The Unbearable Accuracy of Stereotypes. A longer treatment can be found in his 2012 book Social Perception and Social Reality: Why Accuracy Dominates Bias and Self-Fulfilling Prophecy (libgen).

Occupational success and cognitive ability

Society is more or less a semi-stable hierarchy biased on mostly inherited personality traits, cognitive ability as well as some family-based advantage. This shows up in the examination of surnames over time in many countries, as documented in Gregory Clark’s book The Son Also Rises: Surnames and the History of Social Mobility (libgen). One example:

sweden stability

Briefly put, surnames are kind of an extended family and they tend to keep their standing over time. They regress towards the mean (not the statistical kind!), but slowly. This is due to outmarrying (marrying people from lower classes) and genetic regression (i.e. predicted via breeder’s equation and due to the fact that narrow heritability and shared environment does not add up to 1).

It also shows up when educational attainment is directly examined with behavioral genetic methods. We reviewed the literature recently:

How do we find out whether g is causally related to later socioeconomic status? There are at least five lines of evidence: First, g and socioeconomic status correlate in adulthood. This has consistently been found for so many years that it hardly bears repeating[22, 23]. Second, in longitudinal studies, childhood g is a good correlate of adult socioeconomic status. A recent meta-analysis of longitudinal studies found that g was a better correlate of adult socioeconomic status and income than was parental socioeconomic status[24]. Third, there is a genetic overlap of causes of g and socioeconomic status and income[25, 26, 27, 28]. Fourth, multiple regression analyses show that IQ is a good predictor of future socioeconomic status, income and more, even controlling for parental income and the like[29]. Fifth, comparisons between full-siblings reared together show that those with higher IQ tend to do better in society. This cannot be attributed to shared environmental factors since these are the same for both siblings[30, 31].

I’m not aware of any behavioral genetic study of occupational success itself, but that may exist somewhere. (The scientific literature is basically a very badly standardized, difficult to search database.) But clearly, occupational success is closely related to income, educational attainment, cognitive ability and certain personality traits, all of which show substantial heritability and some of which are known to correlate genetically.

Occupations and cognitive ability

An old line of research shows that there is indeed a stable hierarchy in occupations’ mean and minimum cognitive ability levels. One good review of this is Meritocracy, Cognitive Ability,
and the Sources of Occupational Success, a working paper from 2002. I could not find a more recent version. The paper itself is somewhat antagonistic against the idea (the author hates psychometricians, in particular dislikes Herrnstein and Murray, as well as Jensen) but it does neatly summarize a lot of findings.

occu IQ 1

occu IQ 2

occu IQ 3

occu IQ 4

occu IQ 5

occu IQ 6

occu IQ 7

The last one is from Gottfredson’s book chapter g, jobs, and life (her site, better version).

Occupations and cognitive ability in preparation

Furthermore, we can go a step back from the above and find SAT scores (almost an IQ test) by college majors (more numbers here). These later result in people working in different occupations, altho the connection is not always a simple one-to-one, but somewhere between many-to-many and one-to-one, we might call it a few to a few. Some occupations only recruit persons with particular degrees — doctors must have degrees in medicine — while others are flexible within limits. Physics majors often don’t work with physics at their level of competence, but instead work as secondary education teachers, in the finance industry, as programmers, as engineers and of course sometimes as physicists of various kinds such as radiation specialists at hospitals and meteorologists. But still, physicists don’t often work as child carers or psychologists, so there is in general a strong connection between college majors and occupations.

There is some stereotype research into college majors. For instance, a recently popularized study showed that beliefs about intellectual requirements of college majors correlated with female% of the field, as in, the harder fields perceived to be more difficult had fewer women. In fact, the perceived difficulty of the field probably just mostly proxies the actual difficulty of the field, as measured by the mean SAT/ACT score of the students. However, no one seems to have actually correlated the SAT scores with the perceived difficulty, which is the correlation that is the most relevant for stereotype accuracy research.

There is a catch, however. If one analyses the SAT subtests vs. gender%, one sees that it is mostly the quantitative part of the SAT that gives rise to the SAT x gender% correlation. One can also see that the gender% correlates with median income by major.

quant-by-college-major-gender verbal-by-college-major-gender

Stereotypes about occupations and their cognitive ability

Finally, we get to the central question. If we ask people to estimate the cognitive ability of persons by occupation and then correlate this with the actual cognitive ability, what do we get? Jensen summarizes some results in his 1980 book Bias in Mental Testing (p. 339). I mark the most important passages.

People’s average ranking of occupations is much the same regardless of the basis on which they were told to rank them. The well-known Barr scale of occupations was constructed by asking 30 “ psychological judges” to rate 120 specific occupations, each definitely and concretely described, on a scale going from 0 to 100 according to the level of general intelligence required for ordinary success in the occupation. These judgments were made in 1920. Forty-four years later, in 1964, the National Opinion Research Center (NORC), in a large public opinion poll, asked many people to rate a large number of specific occupations in terms of their subjective opinion of the prestige of each occupation relative to all of the others. The correlation between the 1920 Barr ratings based on the average subjectively estimated intelligence requirements of the various occupations and the 1964 NORC ratings based on the average subjective opined prestige of the occupations is .91. The 1960 U.S. Census o f Population: Classified Index o f Occupations and Industries assigns each of several hundred occupations a composite index score based on the average income and educational level prevailing in the occupation. This index correlates .81 with the Barr subjective intelligence ratings and .90 with the NORC prestige ratings.

Rankings of the prestige of 25 occupations made by 450 high school and college students in 1946 showed the remarkable correlation of .97 with the rankings of the same occupations made by students in 1925 (Tyler, 1965, p. 342). Then, in 1949, the average ranking of these occupations by 500 teachers college students correlated .98 with the 1946 rankings by a different group of high school and college students. Very similar prestige rankings are also found in Britain and show a high degree of consistency across such groups as adolescents and adults, men and women, old and young, and upper and lower social classes. Obviously people are in considerable agreement in their subjective perceptions of numerous occupations, perceptions based on some kind of amalagam of the prestige image and supposed intellectual requirements of occupations, and these are highly related to such objective indices as the typical educational level and average income of the occupation. The subjective desirability of various occupations is also a part of the picture, as indicated by the relative frequencies of various occupational choices made by high school students. These frequencies show scant correspondence to the actual frequencies in various occupations; high-status occupations are greatly overselected and low-status occupations are seldom selected.

How well do such ratings of occupations correlate with the actual IQs of the persons in the rated occupations? The answer depends on whether we correlate the occupational prestige ratings with the average IQs in the various occupations or with the IQs of individual persons. The correlations between average prestige ratings and average IQs in occupations are very high— .90 to .95—when the averages are based on a large number of raters and a wide range of rated occupations. This means that the average of many people’s subjective perceptions conforms closely to an objective criterion, namely, tested IQ. Occupations with the highest status ratings are the learned professions—physician, scientist, lawyer, accountant, engineer, and other occupations that involve high educational requirements and highly developed skills, usually of an intellectual nature. The lowest-rated occupations are unskilled manual labor that almost any able-bodied person could do with very little or no prior training or experience and that involves minimal responsibility for decisions or supervision.

The correlation between rated occupational status and individual IQs ranges from about .50 to .70 in various studies. The results of such studies are much the same in Britain, the Netherlands, and the Soviet Union as in the United States, where the results are about the same for whites and blacks. The size of the correlation, which varies among different samples, seems to depend mostly on the age of the persons whose IQs are correlated with occupational status. IQ and occupational status are correlated .50 to .60 for young men ages 18 to 26 and about .70 for men over 40. A few years can make a big difference in these correlations. The younger men, of course, have not all yet attained their top career potential, and some of the highest-prestige occupations are not even represented in younger age groups. Judges, professors, business executives, college presidents, and the like are missing occupational categories in the studies based on young men, such as those drafted into the armed forces (e.g., the classic study of Harrell & Harrell, 1945).

I predict that there is a lot of delicious low-hanging, ripe research fruit ready for harvest in this area if one takes a day or ten to dig up some data and read thru older papers, books and reports.

Let’s test that. Since we don’t have the original data, we can’t use that. We can however use open datasets. I like to use Wicherts’ dataset. So let’s analyze!

p_load(kirkegaard, psych, stringr)

#load wicherts data
w = read.csv("wicherts_data.csv", sep = ";")
w = subset(w, select = c("ravenscore", "lre_cor", "nse_cor", "voc_cor", "van_cor", "hfi_cor", "ari_cor", "Zgpa"))

#remove missing
w = na.omit(w)

w = std_df(w)

#subset the subtests
w_subtests = subset(w, select = c("ravenscore", "lre_cor", "nse_cor", "voc_cor", "van_cor", "hfi_cor", "ari_cor"))

#factor analyze
fa = fa(w_subtests)

plot_loadings(fa) + scale_x_continuous(breaks = seq(0, 1, .05))

#save scores
w_subtests$g = as.numeric(fa$scores)

w_res = residualize_DF(w_subtests, "g")

#include GPA
w_res$GPA = w$Zgpa


#predict GPA
fits = lm_beta_matrix(dependent = "GPA",
                      predictors = colnames(w_res)[-9],
                      data = w_res, return_models = "n")


#why does the last model have NA for one variable?
model = str_c("GPA ~ ", str_c(colnames(w_res)[-9], collapse = " + "))
fit = lm(model, w_res)




ravenscore lre_cor nse_cor voc_cor van_cor hfi_cor ari_cor g GPA
ravenscore 1 -0.125 -0.201 -0.115 0.057 0.022 -0.294 0 -0.028
lre_cor -0.125 1 -0.316 -0.143 -0.195 -0.23 -0.288 0 -0.074
nse_cor -0.201 -0.316 1 -0.206 -0.381 -0.103 0.099 0 -0.116
voc_cor -0.115 -0.143 -0.206 1 0.198 -0.141 -0.207 0 0.08
van_cor 0.057 -0.195 -0.381 0.198 1 -0.081 -0.406 0 0.137
hfi_cor 0.022 -0.23 -0.103 -0.141 -0.081 1 -0.233 0 0.037
ari_cor -0.294 -0.288 0.099 -0.207 -0.406 -0.233 1 0 0.007
g 0 0 0 0 0 0 0 1 0.334
GPA -0.028 -0.074 -0.116 0.08 0.137 0.037 0.007 0.334 1


Model fits

Model # ravenscore lre_cor nse_cor voc_cor van_cor hfi_cor ari_cor g r2.adj.
1 -0.028               -0.003
2 -0.074 0.002
3 -0.116 0.01
4 0.08 0.003
5 0.137 0.015
6 0.037 -0.002
7 0.007 -0.003
8 0.334 0.108
9 -0.038 -0.079 0
10 -0.054 -0.127 0.009
11 -0.019 0.078 0
12 -0.036 0.139 0.013
13 -0.029 0.038 -0.005
14 -0.029 -0.001 -0.006
15 -0.028 0.334 0.106
16 -0.123 -0.155 0.02
17 -0.064 0.071 0.004
18 -0.049 0.127 0.014
19 -0.069 0.021 -0.001
20 -0.078 -0.015 -0.001
21 -0.074 0.334 0.111
22 -0.104 0.059 0.01
23 -0.075 0.108 0.017
24 -0.114 0.026 0.007
25 -0.118 0.019 0.007
26 -0.116 0.334 0.119
27 0.055 0.126 0.015
28 0.087 0.05 0.002
29 0.086 0.025 0
30 0.08 0.334 0.112
31 0.141 0.049 0.014
32 0.168 0.075 0.017
33 0.137 0.334 0.124
34 0.041 0.017 -0.005
35 0.037 0.334 0.107
36 0.007 0.334 0.105
37 -0.082 -0.14 -0.177 0.023
38 -0.029 -0.068 0.067 0.001
39 -0.043 -0.054 0.129 0.013
40 -0.038 -0.074 0.021 -0.003
41 -0.049 -0.09 -0.033 -0.003
42 -0.038 -0.079 0.334 0.109
43 -0.046 -0.115 0.052 0.008
44 -0.052 -0.086 0.107 0.016
45 -0.054 -0.125 0.026 0.007
46 -0.053 -0.127 0.004 0.006
47 -0.054 -0.127 0.334 0.119
48 -0.03 0.052 0.128 0.012
49 -0.02 0.085 0.05 -0.001
50 -0.013 0.083 0.021 -0.003
51 -0.019 0.078 0.334 0.109
52 -0.038 0.143 0.05 0.012
53 -0.017 0.166 0.07 0.014
54 -0.036 0.139 0.334 0.122
55 -0.027 0.04 0.009 -0.008
56 -0.029 0.038 0.334 0.104
57 -0.029 -0.001 0.334 0.103
58 -0.115 -0.146 0.034 0.018
59 -0.097 -0.119 0.072 0.021
60 -0.125 -0.157 -0.008 0.017
61 -0.127 -0.155 -0.014 0.017
62 -0.123 -0.155 0.334 0.129
63 -0.043 0.051 0.118 0.013
64 -0.055 0.078 0.036 0.001
65 -0.062 0.072 0.004 0
66 -0.064 0.071 0.334 0.113
67 -0.039 0.133 0.039 0.012
68 -0.024 0.158 0.065 0.014
69 -0.049 0.127 0.334 0.123
70 -0.072 0.018 -0.009 -0.005
71 -0.069 0.021 0.334 0.108
72 -0.078 -0.015 0.334 0.108
73 -0.068 0.046 0.102 0.015
74 -0.099 0.065 0.036 0.008
75 -0.106 0.065 0.031 0.007
76 -0.104 0.059 0.334 0.119
77 -0.069 0.114 0.039 0.015
78 -0.07 0.139 0.071 0.017
79 -0.075 0.108 0.334 0.126
80 -0.116 0.031 0.026 0.004
81 -0.114 0.026 0.334 0.116
82 -0.118 0.019 0.334 0.116
83 0.063 0.129 0.057 0.015
84 0.067 0.158 0.085 0.017
85 0.055 0.126 0.334 0.124
86 0.098 0.061 0.042 0
87 0.087 0.05 0.334 0.111
88 0.086 0.025 0.334 0.109
89 0.183 0.075 0.099 0.018
90 0.141 0.049 0.334 0.123
91 0.168 0.075 0.334 0.126
92 0.041 0.017 0.334 0.104
93 -0.078 -0.135 -0.171 0.017 0.02
94 -0.076 -0.116 -0.144 0.064 0.023
95 -0.082 -0.144 -0.179 -0.012 0.02
96 -0.099 -0.158 -0.181 -0.05 0.022
97 -0.082 -0.14 -0.177 0.334 0.133
98 -0.036 -0.048 0.045 0.121 0.011
99 -0.028 -0.059 0.074 0.035 -0.002
100 -0.033 -0.072 0.064 -0.01 -0.003
101 -0.029 -0.068 0.067 0.334 0.11
102 -0.042 -0.044 0.134 0.039 0.011
103 -0.026 -0.032 0.154 0.053 0.011
104 -0.043 -0.054 0.129 0.334 0.122
105 -0.048 -0.085 0.012 -0.029 -0.006
106 -0.038 -0.074 0.021 0.334 0.106
107 -0.049 -0.09 -0.033 0.334 0.107
108 -0.046 -0.079 0.039 0.102 0.014
109 -0.045 -0.11 0.058 0.035 0.006
110 -0.04 -0.115 0.056 0.018 0.005
111 -0.046 -0.115 0.052 0.334 0.118
112 -0.052 -0.08 0.113 0.039 0.014
113 -0.034 -0.078 0.133 0.059 0.015
114 -0.052 -0.086 0.107 0.334 0.125
115 -0.051 -0.125 0.028 0.011 0.003
116 -0.054 -0.125 0.026 0.334 0.116
117 -0.053 -0.127 0.004 0.334 0.115
118 -0.03 0.059 0.132 0.057 0.012
119 -0.005 0.066 0.158 0.084 0.014
120 -0.03 0.052 0.128 0.334 0.122
121 -0.007 0.096 0.06 0.039 -0.003
122 -0.02 0.085 0.05 0.334 0.108
123 -0.013 0.083 0.021 0.334 0.106
124 -0.013 0.182 0.074 0.095 0.015
125 -0.038 0.143 0.05 0.334 0.122
126 -0.017 0.166 0.07 0.334 0.123
127 -0.027 0.04 0.009 0.334 0.101
128 -0.091 -0.112 0.03 0.071 0.018
129 -0.115 -0.145 0.034 0.001 0.014
130 -0.117 -0.146 0.033 -0.005 0.014
131 -0.115 -0.146 0.034 0.334 0.127
132 -0.094 -0.116 0.075 0.01 0.017
133 -0.081 -0.11 0.092 0.032 0.018
134 -0.097 -0.119 0.072 0.334 0.13
135 -0.132 -0.158 -0.014 -0.019 0.014
136 -0.125 -0.157 -0.008 0.334 0.126
137 -0.127 -0.155 -0.014 0.334 0.127
138 -0.03 0.059 0.123 0.049 0.012
139 -0.011 0.065 0.155 0.08 0.014
140 -0.043 0.051 0.118 0.334 0.123
141 -0.045 0.085 0.044 0.022 -0.002
142 -0.055 0.078 0.036 0.334 0.111
143 -0.062 0.072 0.004 0.334 0.11
144 0.012 0.189 0.08 0.106 0.015
145 -0.039 0.133 0.039 0.334 0.122
146 -0.024 0.158 0.065 0.334 0.123
147 -0.072 0.018 -0.009 0.334 0.105
148 -0.059 0.054 0.108 0.047 0.014
149 -0.061 0.058 0.135 0.08 0.017
150 -0.068 0.046 0.102 0.334 0.125
151 -0.1 0.076 0.048 0.044 0.006
152 -0.099 0.065 0.036 0.334 0.117
153 -0.106 0.065 0.031 0.334 0.117
154 -0.059 0.157 0.065 0.092 0.018
155 -0.069 0.114 0.039 0.334 0.124
156 -0.07 0.139 0.071 0.334 0.127
157 -0.116 0.031 0.026 0.334 0.114
158 0.083 0.175 0.09 0.117 0.021
159 0.063 0.129 0.057 0.334 0.124
160 0.067 0.158 0.085 0.334 0.127
161 0.098 0.061 0.042 0.334 0.109
162 0.183 0.075 0.099 0.334 0.128
163 -0.072 -0.112 -0.139 0.015 0.063 0.019
164 -0.079 -0.138 -0.174 0.015 -0.009 0.016
165 -0.1 -0.158 -0.182 -0.002 -0.05 0.018
166 -0.078 -0.135 -0.171 0.017 0.334 0.13
167 -0.075 -0.115 -0.143 0.065 0.003 0.019
168 -0.083 -0.127 -0.152 0.052 -0.018 0.019
169 -0.076 -0.116 -0.144 0.064 0.334 0.133
170 -0.106 -0.173 -0.19 -0.034 -0.063 0.019
171 -0.082 -0.144 -0.179 -0.012 0.334 0.13
172 -0.099 -0.158 -0.181 -0.05 0.334 0.132
173 -0.035 -0.035 0.053 0.125 0.048 0.01
174 -0.01 -0.015 0.063 0.153 0.075 0.011
175 -0.036 -0.048 0.045 0.121 0.334 0.121
176 -0.024 -0.054 0.077 0.038 0.009 -0.005
177 -0.028 -0.059 0.074 0.035 0.334 0.108
178 -0.033 -0.072 0.064 -0.01 0.334 0.107
179 -0.01 0.008 0.186 0.078 0.1 0.012
180 -0.042 -0.044 0.134 0.039 0.334 0.12
181 -0.026 -0.032 0.154 0.053 0.334 0.121
182 -0.048 -0.085 0.012 -0.029 0.334 0.104
183 -0.044 -0.07 0.046 0.107 0.046 0.012
184 -0.022 -0.067 0.053 0.131 0.072 0.014
185 -0.046 -0.079 0.039 0.102 0.334 0.124
186 -0.034 -0.108 0.067 0.044 0.032 0.003
187 -0.045 -0.11 0.058 0.035 0.334 0.116
188 -0.04 -0.115 0.056 0.018 0.334 0.115
189 -0.028 -0.066 0.152 0.062 0.082 0.015
190 -0.052 -0.08 0.113 0.039 0.334 0.124
191 -0.034 -0.078 0.133 0.059 0.334 0.125
192 -0.051 -0.125 0.028 0.011 0.334 0.113
193 0.004 0.083 0.176 0.091 0.118 0.018
194 -0.03 0.059 0.132 0.057 0.334 0.122
195 -0.005 0.066 0.158 0.084 0.334 0.124
196 -0.007 0.096 0.06 0.039 0.334 0.106
197 -0.013 0.182 0.074 0.095 0.334 0.125
198 -0.083 -0.105 0.035 0.076 0.018 0.015
199 -0.065 -0.095 0.042 0.098 0.046 0.016
200 -0.091 -0.112 0.03 0.071 0.334 0.128
201 -0.118 -0.146 0.032 -0.002 -0.006 0.011
202 -0.115 -0.145 0.034 0.001 0.334 0.124
203 -0.117 -0.146 0.033 -0.005 0.334 0.124
204 -0.053 -0.089 0.12 0.039 0.059 0.015
205 -0.094 -0.116 0.075 0.01 0.334 0.127
206 -0.081 -0.11 0.092 0.032 0.334 0.128
207 -0.132 -0.158 -0.014 -0.019 0.334 0.124
208 0.044 0.094 0.195 0.11 0.144 0.019
209 -0.03 0.059 0.123 0.049 0.334 0.122
210 -0.011 0.065 0.155 0.08 0.334 0.124
211 -0.045 0.085 0.044 0.022 0.334 0.108
212 0.012 0.189 0.08 0.106 0.334 0.125
213 -0.044 0.075 0.157 0.082 0.11 0.019
214 -0.059 0.054 0.108 0.047 0.334 0.124
215 -0.061 0.058 0.135 0.08 0.334 0.127
216 -0.1 0.076 0.048 0.044 0.334 0.116
217 -0.059 0.157 0.065 0.092 0.334 0.128
218 0.083 0.175 0.09 0.117 0.334 0.131
219 -0.071 -0.109 -0.136 0.017 0.065 0.007 0.016
220 -0.078 -0.12 -0.145 0.011 0.056 -0.011 0.016
221 -0.072 -0.112 -0.139 0.015 0.063 0.334 0.13
222 -0.117 -0.188 -0.201 -0.024 -0.045 -0.077 0.016
223 -0.079 -0.138 -0.174 0.015 -0.009 0.334 0.127
224 -0.1 -0.158 -0.182 -0.002 -0.05 0.334 0.128
225 -0.09 -0.142 -0.163 0.038 -0.014 -0.032 0.016
226 -0.075 -0.115 -0.143 0.065 0.003 0.334 0.13
227 -0.083 -0.127 -0.152 0.052 -0.018 0.334 0.13
228 -0.106 -0.173 -0.19 -0.034 -0.063 0.334 0.129
229 0.023 0.056 0.101 0.201 0.118 0.16 0.016
230 -0.035 -0.035 0.053 0.125 0.048 0.334 0.12
231 -0.01 -0.015 0.063 0.153 0.075 0.334 0.121
232 -0.024 -0.054 0.077 0.038 0.009 0.334 0.105
233 -0.01 0.008 0.186 0.078 0.1 0.334 0.122
234 -0.009 -0.046 0.072 0.155 0.08 0.106 0.016
235 -0.044 -0.07 0.046 0.107 0.046 0.334 0.123
236 -0.022 -0.067 0.053 0.131 0.072 0.334 0.124
237 -0.034 -0.108 0.067 0.044 0.032 0.334 0.114
238 -0.028 -0.066 0.152 0.062 0.082 0.334 0.125
239 0.004 0.083 0.176 0.091 0.118 0.334 0.128
240 0.015 -0.034 0.08 0.168 0.09 0.121 0.016
241 -0.083 -0.105 0.035 0.076 0.018 0.334 0.125
242 -0.065 -0.095 0.042 0.098 0.046 0.334 0.126
243 -0.118 -0.146 0.032 -0.002 -0.006 0.334 0.121
244 -0.053 -0.089 0.12 0.039 0.059 0.334 0.126
245 0.044 0.094 0.195 0.11 0.144 0.334 0.129
246 -0.044 0.075 0.157 0.082 0.11 0.334 0.13
247 -0.071 -0.109 -0.136 0.017 0.065 0.007 0.016
248 -0.071 -0.109 -0.136 0.017 0.065 0.007 0.334 0.127
249 -0.078 -0.12 -0.145 0.011 0.056 -0.011 0.334 0.127
250 -0.117 -0.188 -0.201 -0.024 -0.045 -0.077 0.334 0.127
251 -0.09 -0.142 -0.163 0.038 -0.014 -0.032 0.334 0.127
252 0.023 0.056 0.101 0.201 0.118 0.16 0.334 0.127
253 -0.009 -0.046 0.072 0.155 0.08 0.106 0.334 0.127
254 0.015 -0.034 0.08 0.168 0.09 0.121 0.334 0.127
255 -0.071 -0.109 -0.136 0.017 0.065 0.007 0.334 0.127

[Bonus points to whoever can explain why the last ari_cor has a missing value in the last model. I checked. It is not a problem with my function. I don’t know.]

So Timofey Pnin is right. The beta does stay exactly the same across models, at least two 3 digits.

We may also note that adding the other predictors did not have much effect: g alone (model #8) R2 adj. = .108, best model according to R2 adj. = 0.132 (#97). Notice how this model has negative betas for the other items. In other words, one is better off with lower scores. Surely that can’t be right. It is probably just a fluke due to overfitting…

Testing overfitting

We can test overfitting using lasso regression (read this book, seriously, it’s a great book!). Because lasso regression is indeterministic, we repeat it a large number of times and examine the overall results.

#lasso regression
fits_2 = MOD_repeat_cv_glmnet(df = w_res,
                              dependent = "GPA",
                              predictors = colnames(w_res)[-9],
                              runs = 500)


Lasso results

ravenscore lre_cor nse_cor voc_cor van_cor hfi_cor ari_cor g
mean 0 0 0 0 0 0 0 0.096
median 0 0 0 0 0 0 0 0.104
sd 0 0 0 0 0.001 0 0 0.033
fraction_zeroNA 1 1 1 1 0.996 1 1 0.01


The lasso confirms our suspicions. The non-g variables were fairly useless, their apparent usefulness due to overfitting. g retained its usefulness in 99% of the runs. The most promising of the other candidates was only useful in .04% of runs.

This is probably worth writing into a short paper. Contact me if you are willing to do this. I will help you, but I don’t have time to write it all myself.

G.M. IQ & Economic growth

I noted down some comments while reading it.

In Table 1, Dominican birth cohort is reversed.


“0.70 and 0.80 in world-wide country samples. Figure 1 gives an impression of

this relationship.”


Figure 1 shows regional IQs, not GDP relationships.

“We still depend on these descriptive methods of quantitative genetics because

only a small proportion of individual variation in general intelligence and

school achievement can be explained by known genetic polymorphisms (e.g.,

Piffer, 2013a,b; Rietveld et al, 2013).”


We don’t. Modern BG studies can confirm A^2 estimates directly from the genes.


Davies, G., Tenesa, A., Payton, A., Yang, J., Harris, S. E., Liewald, D., … & Deary, I. J. (2011). Genome-wide association studies establish that human intelligence is highly heritable and polygenic. Molecular psychiatry, 16(10), 996-1005.

Marioni, R. E., Davies, G., Hayward, C., Liewald, D., Kerr, S. M., Campbell, A., … & Deary, I. J. (2014). Molecular genetic contributions to socioeconomic status and intelligence. Intelligence, 44, 26-32.

Results are fairly low tho, in the 20’s, presumably due to non-additive heritability and rarer genes.


“Even in modern societies, the heritability of

intelligence tends to be higher for children from higher socioeconomic status

(SES) families (Turkheimer et al, 2003; cf. Nagoshi and Johnson, 2005; van

der Sluis et al, 2008). Where this is observed, most likely environmental

conditions are of similar high quality for most high-SES children but are more

variable for low-SES children. “


Or maybe not. There are also big studies that don’t find this interaction effect.


“Schooling has

only a marginal effect on growth when intelligence is included, consistent with

earlier results by Weede & Kämpf (2002) and Ram (2007).”

In the regression model of all countries, schooling has a larger beta than IQ does (.158 and .125). But these appear to be unstandardized values, so they are not readily comparable.

“Also, earlier studies that took account of

earnings and cognitive test scores of migrants in the host country or IQs in

wealthy oil countries have concluded that there is a substantial causal effect of

IQ on earnings and productivity (Christainsen, 2013; Jones & Schneider,



National IQs were also found to predict migrant income, as well as most other socioeconomic traits, in Denmark and Norway (and Finland and the Netherland).

Kirkegaard, E. O. W. (2014). Crime, income, educational attainment and employment among immigrant groups in Norway and Finland. Open Differential Psychology.

Kirkegaard, E. O. W., & Fuerst, J. (2014). Educational attainment, income, use of social benefits, crime rate and the general socioeconomic factor among 71 immigrant groups in Denmark. Open Differential Psychology.



Figures 3 A-C are of too low quality.



“Allocation of capital resources has been an

element of classical growth theory (Solow, 1956). Human capital theory

emphasizes that individuals with higher intelligence tend to have lower

impulsivity and lower time preference (Shamosh & Gray, 2008). This is

predicted to lead to higher savings rates and greater resource allocation to

investment relative to consumption in countries with higher average



Time preference data for 45 countries are given by:

Wang, M., Rieger, M. O., & Hens, T. (2011). How time preferences differ: evidence from 45 countries.

They are in the megadataset from version 1.7f

Correlations among some variables of interest:

             SlowTimePref   IQ lgGDP
SlowTimePref         1.00         0.45         0.48 0.57  0.64         0.45         1.00         0.89 0.55  0.59         0.48         0.89         1.00 0.65  0.66
IQ                   0.57         0.55         0.65 1.00  0.72
lgGDP                0.64         0.59         0.66 0.72  1.00

             SlowTimePref  IQ lgGDP
SlowTimePref          273           32           12  45    40           32          273           20  68    58           12           20          273  23    20
IQ                     45           68           23 273   169
lgGDP                  40           58           20 169   273

So time prefs predict income in DK and NO only slightly worse than national IQs or lgGDP.



“Another possible mediator of intelligence effects that is difficult to

measure at the country level is the willingness and ability to cooperate. A

review by Jones (2008) shows that cooperativeness, measured in the Prisoner‟s

dilemma game, is positively related to intelligence. This correlate of

intelligence may explain some of the relationship of intelligence with

governance. Other likely mediators of the intelligence effect include less red

tape and restrictions on economic activities (“economic freedom”), higher

savings and/or investment, and technology adoption in developing countries.”


There are data for IQ and trust too. Presumably trust is closely related to willingness to cooperate.

Carl, N. (2014). Does intelligence explain the association between generalized trust and economic development? Intelligence, 47, 83–92. doi:10.1016/j.intell.2014.08.008



“There is no psychometric evidence for rising intelligence before that time

because IQ tests were introduced only during the first decade of the 20th

century, but literacy rates were rising steadily after the end of the Middle Age

in all European countries for which we have evidence (Mitch, 1992; Stone,

1969), and the number of books printed per capita kept rising (Baten & van

Zanden, 2008).”


There’s also age heaping scores which are a crude measure of numeracy. AH scores for 1800 to 1970 are in the megadataset. They have been going up for centuries too just like literacy scores. See:

A’Hearn, B., Baten, J., & Crayen, D. (2009). Quantifying quantitative literacy: Age heaping and the history of human capital. The Journal of Economic History, 69(03), 783–808.



“Why did this spiral of economic and cognitive growth take off in Europe

rather than somewhere else, and why did it not happen earlier, for example in

classical Athens or the Roman Empire? One part of the answer is that this

process can start only when technologies are already in place to translate rising

economic output into rising intelligence. The minimal requirements are a

writing system that is simple enough to be learned by everyone without undue

effort, and a means to produce and disseminate written materials: paper, and

the printing press. The first requirement had been present in Europe and the

Middle East (but not China) since antiquity, and the second was in place in

Europe from the 15thcentury. The Arabs had learned both paper-making and

printing from the Chinese in the 13thcentury (Carter, 1955), but showed little

interest in books. Their civilization was entering into terminal decline at about

that time (Huff, 1993). “


Are there no FLynn effects in China? They still have a difficult writing system.


“Most important is that Flynn effect gains have been decelerating in recent

years. Recent losses (anti-Flynn effects) were noted in Britain, Denmark,

Norway and Finland. Results for the Scandinavian countries are based on

comprehensive IQ testing of military conscripts aged 18-19. Evidence for

losses among British teenagers is derived from the Raven test (Flynn, 2009)

and Piagetian tests (Shayer & Ginsburg, 2009). These observations suggest

that for cohorts born after about 1980, the Flynn effect is ending or has ended

in many and perhaps most of the economically most advanced countries.

Messages from the United States are mixed, with some studies reporting

continuing gains (Flynn, 2012) and others no change (Beaujean & Osterlind,



These are confounded with immigration of low-g migrants however. Maybe the FLynn effect is still there, just being masked by dysgenics + low-g immigration.



“The unsustainability of this situation is obvious. Estimating that one third

of the present IQ differences between countries can be attributed to genetics,

and adding this to the consequences of dysgenic fertility within countries,

leaves us with a genetic decline of between 1 and 2 IQ points per generation

for the entire world population. This decline is still more than offset by Flynn

effects in less developed countries, and the average IQ of the world‟s

population is still rising. This phase of history will end when today‟s

developing countries reach the end of the Flynn effect. “Peak IQ” can

reasonably be expected in cohorts born around the mid-21stcentury. The

assumptions of the peak IQ prediction are that (1) Flynn effects are limited by

genetic endowments, (2) some countries are approaching their genetic limits

already, and others will fiollow, and (3) today‟s patterns of differential fertility

favoring the less intelligent will persist into the foreseeable future. “


It is possible that embryo selection for higher g will kick in and change this.

Shulman, C., & Bostrom, N. (2014). Embryo Selection for Cognitive Enhancement: Curiosity or Game-changer? Global Policy, 5(1), 85–92. doi:10.1111/1758-5899.12123



“Fertility differentials between countries lead to replacement migration: the

movement of people from high-fertility countries to low-fertility countries,

with gradual replacement of the native populations in the low-fertility

countries (Coleman, 2002). The economic consequences depend on the

quality of the migrants and their descendants. Educational, cognitive and

economic outcomes of migrants are influenced heavily by prevailing

educational, cognitive and economic levels in the country of origin (Carabaña,

2011; Kirkegaard, 2013; Levels & Dronkers, 2008), and by the selectivity of

migration. Brain drain from poor to prosperous countries is extensive already,

for example among scientists (Franzoni, Scellato & Stephan, 2012; Hunter,

Oswald & Charlton, 2009). “


There are quite a few more papers on the spatial transferability hypothesis. I have 5 papers on this alone in ODP:

But there’s also yet unpublished data for crime in Netherlands and more crime data for Norway. Papers based off these data are on their way.


Jan te Nijenhuis, Birthe Jongeneel-Grimen and Emil O. W. Kirkegaard. Are Headstart gains on the g factor?: A meta-analysis. Intelligence. Volume 46, September–October 2014, Pages 209–215


If all goes well, it may be the only paper i publish in Intelligence.


Primary author is a cool guy: The second author is his former grad student i think.

Recently, the twin-control design has been used to test causal models (e.g. exercise→happiness, exercise→¬depression/anxiety symptoms, casual sex→depression/suicidal thoughts). The theory is simple. Suppose we do a standard cross-sectional design study and find that X and Y are correlated. Suppose we suspect that X causes Y. Then, if X causes Y, then one would expect to see a relationship within identical twin pairs for X and Y. If the correlation between X and Y is due to shared genetics, then it will not be correlated within identical twin pairs (baring any de novo mutation being responsible for it). If it is found to be correlated within identical twins, then the education model may be true but also some developmental models relying on non-education environmentally caused differences as well as de novo mutation genetic models.

Did anyone do a study like this? I haven’t seen it, but it is quite simple to do. The only thing needed is a dataset with identical twins, educational attainment/years in school and some g proxy. Maybe NLSY? If you know of a dataset, contact me and we will try.

So i kept finding references to this book in papers, so i decided to read it. It is a quick read introducing behavior genetics and the results from it to lay readers and perhaps policy makers. The book is overly long (200) for its content, it cud easily have been cut 30 pages. The book itself contains not much new to people familiar with the field (i.e. me), however there are some references that were interesting and unknown to me. It may pay for the expert to simply skim the reference lists for each chapter and read those papers instead.

The main thrust of the book is what policies we shud implement becus of our ‘new’ behavioral genetic knowledge. Basically the authors think that we need to add more choice to schools becus everybody is different and we want to use the gene-environment correlations to improve results. It is hard to disagree with this. They go on about how labeling is bad, but obviously labeling is useful for talking about things.

If one is interested in school policy then reading this book may be worth it, especially if one is a layman. If one is interested in learning behavior genetics, read something else (e.g. Plomin’s 2012 textbook)

Posted on reddit.


This is your best film yet, and that says something.

For automatization for clinical decisions, it has been known for decades that simple algorithms are better than humans. This has so far not been put to much practice, but it will eventually. See review article: Grove, W. M., Zald, D. H., Lebow, B. S., Snitz, B. E., & Nelson, C. (2000). Clinical versus mechanical prediction: a meta-analysis.[1] Psychological assessment, 12(1), 19.

There is only one temporary solution for this problem. It is to make humans smarter. I say temporary because these new smarter humans will quickly make robots even smarter and so they can replace even the new smarter humans.

How to make humans more intelligent? The only effective way to do that is to use applied human genetics aka. eugenics. This is because general intelligence (g-factor) is about 80% heritable in adults (and pretty much everything else is also moderately to highly heritable). There are two things we must do: 1) Find the genes for g. This effort is underway and we have found a few SNPs so far.[1-2] It is estimated that there are about 1k-10k genes for g. 2) Find out how to apply this genetic knowledge in practice to make both existing humans and the new ones smarter. The first effective technology for this is embryo selection[2] . Perhaps CRISPR[3] can work for existing humans.

  1. Rietveld, C.A., Medland, S.E., Derringer, J., Yang, K., Esko, T., et al. (2013). GWAS of 126,559 individuals identifies genetic variants associated with educational attainment. Science 340: 1467-1471.
  2. Ward, M.E., McMahon, G., St Pourcain, B., Evans, D.M., Rietveld, C.A., et al. (2014) Genetic Variation Associated with Differential Educational Attainment in Adults Has Anticipated Associations with School Performance in Children. PLoS ONE 9(7): e100248. doi:10.1371/journal.pone.0100248

Downloaded from here:



I came across Sommers years ago when i read her interview here:


It had this bit:


MS. PAGLIA: Well, one of the things that got me pilloried from coast to coast was when I wrote a piece on date rape for Newsday in January of 1991. It got picked up by the wire services, and the torrent of abuse that poured in. I want women to fend for themselves. That essay that I wrote on rape begins with the line “Rape is an outrage that cannot be tolerated in civilized society.” I absolutely abhor this broadening of the idea of rape, which is an atrocity, to those things that go wrong on a date –acquaintances, you know, little things, miscommunications — on pampered elite college campuses.

MS. SOMMERS: I interviewed a young women at the University of Pennsylvania who came in in a short skirt and she was in the Women’s Center, and I think she thought I was one of the sisterhood. And she said, “Oh, I just suffered a mini-rape.” And I said, “What happened?” And she said, “A boy walked by me and said, `Nice legs’.” You know? And that — and this young woman considers this a form of rape!




after having concentrated on studying the scientific side of things:


I started reading more on the polemic and political side of things:


and now the time has come to give feminism itself a closer view. i cant say this was a pleasurable read, it was mostly disturbing. Worse, its from 1994 so who knows how bad it has become since then?! I had to give this 5 out of 5 for opening my eyes to the insanity that goes on in feminist circles. If feminism has indeed been stolen, it is time to denounce it entirely. After all, no one really wants to take away women’s civil rights anyway (except muslims and radical xtians), so there is no need for explicit equity feminism anymore.




In Revolution from Within, Gloria Steinem informs her readers that “in

this country alone . . . about 150,000 females die of anorexia each year.”1

That is mor e than three times the annual numbe r of fatalities from car

accidents for the total population. Steinem refers readers to anothe r fem­

inist best-seller, Naomi Wolf s The Beauty Myth. And in Ms. Wolf s boo k

one again finds the statistic, along with the author’ s outrage. “How, ” she

asks, “would America react to the mass self-immolation by hunge r of its

favorite sons?”2 Although “nothing justifies comparison with the Holo­

caust,” she cannot refrain from making one anyway. “When confronted

with a vast numbe r of emaciated bodies starved not by nature but by

men, one mus t notice a certain resemblance.”3


Where did Ms. Wolf get her figures? Her source is Fasting Girls: The

Emergence of Anorexia Nervosa as a Modern Disease* by Joan Brumberg, a

historian and former director of women’ s studies at Cornel l University.

Brumberg, too, is fully aware of the political significance of the startling

statistic. She point s out that the wome n wh o study eating problems “seek

to demonstrate that these disorders are an inevitable consequence of a

misogynistic society that demeans women.. . by objectifying their bodies.”5

Professor Brumberg, in turn, attributes the figure to the American Anorexia

and Bulimia Association.


I called the American Anorexia and Bulimia Association and spoke to

Dr. Diane Mickley, its president . “We were misquoted,” she said. In a

1985 newsletter the association had referred to 150,000 to 200,000 suf­

ferers (not fatalities) of anorexia nervosa.


What is the correct morbidity rate? Most experts are reluctant to give

exact figures. On e clinician told me that of 1,400 patients she had treated

in ten years, four had died—al l through suicide. The National Center for

Health Statistics reported 101 deaths from anorexia nervosa in 1983 and

67 deaths in 1988.6 Thoma s Dun n of the Division of Vital Statistics at the

National Center for Health Statistics reports that in 1991 there were 54

deaths from anorexia nervosa and no deaths from bulimia. The deaths of

these young wome n are a tragedy, certainly, but in a country of one

hundre d million adul t females, such number s are hardly evidence of a



Yet now the false figure, supporting the view that our “sexist society”

demeans wome n by objectifying their bodies, is widely accepted as true.

Ann Landers repeated it in her syndicated column in Apri l 1992: “Every

year, 150,000 American wome n die from complications associated with

anorexia and bulimia.”7


I sent Naomi Wol f a letter pointing out that Dr. Mickley had said she

was mistaken. Wol f sent me word on February 3, 1993, that she intends

to revise he r figures on anorexia in a later edition of The Beauty Myth.8

Will she actually state that the correct figure is less than one hundred per

year? And wil l she correct the implications she drew from the false report?

For example, wil l she revise her thesis that masses of young women are

being “starved not by nature but by men” and her declaration that

“women mus t claim anorexia as political damage done to us by a social

order that considers our destruction insignificant.. . as Jews identify the

death camps”?9


This is the OPENING of the book. What the fuck. No wonder feminists are batshit insane if they read this and think its true.



Virginia Held, a philosophy professor at the City University of New

York, reported on the feminist conviction that feminist philosopher s are

the initiators of an intellectual revolution comparable to those of “Coper ­

nicus, Darwin, and Freud.”1 9 Indeed, as Held points out , “some feminists

think the latest revolution will be even mor e profound.” According to

Held, the sex/gender system is the controlling insight of this feminist

revolution. Ms. Held tells us of the impact that the discovery of the sex/

gender system has had on feminist theory: “Now that the sex/gender

system has become visible to us , we can see it everywhere.”2 0


One if reminded of the crackpot index:


“40 points for comparing yourself to Galileo, suggesting that a modern-day Inquisition is hard at work on your case, and so on. “



Anyone reading contemporary feminist literature will find a genre of

writing concerned with personal outrage. Professor Kathryn Allen Ra-

buzzi of Syracuse University opens her book Motherself by recounting this

incident :


As I was walking down a sleazy section of Second Avenue in New

York City a few years ago, a voice suddenly intruded on my con­

sciousness: “Hey Mama, spare change?” The words outraged me. . . .

Although I had by then been a mothe r for many years, never till that

momen t had I seen myself as “Mama” in such an impersonal , exter-

nal context . In the man’ s speaking I beheld myself anew. “1 ” disap­

peared, as though turned inside out , and “Mama” took my place.2 1


Ms. Rabuzzi informs us that the panhandler’ s term caused in her a

“shocking dislocation of self.” Similarly, University of Illinois feminist

theorist Sandra Lee Bartky recounts:


It is a fine spring day, and with an utter lack of self-consciousness,

I am bouncing down the street . Suddenly . . . catcalls and whistles

fill the air. These noises are clearly sexual in intent and they are

meant for me; they come from across the street . I freeze. As Sartre

would say, I have been petrified by the gaze of the Other . My face

flushes and my motions become stiff and self-conscious. The body

which only a momen t before I inhabited with such ease now floods

my consciousness. I have been made into an object. . . . Blissfully

unaware, breasts bouncing, eyes on the birds in the trees, I could

have passed by without having been turned to stone. But I mus t be

made to know that I am a “nice piece of ass”: I mus t be made to see

myself as they see me. There is an element of compulsion in . . . this

being-made-to-be-aware of one’s own flesh: like being made to

apologize, it is humiliating. . . . Wha t I describe seems less the spon­

taneous expression of a healthy eroticism than a ritual of subjuga­

tion.2 2


Marilyn French, the author of The War Against Women, finds herself

vulnerable in museums :


Artists appropriate the female body as their subject , thei r possession

. . . assaulting female reality and autonomy. . . . Visiting galleries

and museums (especially the Pompidou Center in Paris) I feel as­

saulted by twentieth-century abstract sculpture that resembles ex­

aggerated female body parts, mainly breasts.2 3


wtf am i reading


the sick part: THESE ARE PROFESSORS!!!


the ultrasick part: THIS WAS BEFORE 1994! ITS WORSE TODAY



This, for example, is wha t Professor Susan McClary, a musicologist at

the University of Minnesota, tells us to listen for in Beethoven’s Ninth

Symphony: “The point of recapitulation in the first movement of the

Ninth is one of the mos t horrifying moment s in music, as the carefully

prepared cadence is frustrated, damming u p energy which finally ex­

plodes in the throttling, murderous rage of a rapist incapable of attaining

release.”2 5 McClary also directs us to be alert to themes of male mastur ­

bation in the music of Richard Strauss and Gustav Mahler.



Seneca Falls focused on specific injustices of the kind that social policy

could repair by making the laws equitable. In thinking about that first

women’ s conference, it is helpful to remembe r the state of the average

American woma n in the mid-nineteent h century. Consider the story of

Hester Vaughan. In 1869, at the age of twenty, she had been deserted by

her husband. She found work in a wealthy Philadelphia home wher e the

man of the house seduced her and, when she became pregnant , fired her .

In a state of terrible indigence, she gave birth alone in an unheated rented

room, collapsing minutes afterward. By the time she was discovered, the

baby had died. She was charged with murder . No lawyer represented her

at her trial, and she was not permitted to testify. An all-male jury found

her guilty, and the judge sentenced her to death.


Elizabeth Cady Stanton and Susan B. Anthony learned of her plight

and organized a campaign to help her. On e protest meeting drew nearly

a thousand women. Here is how the historian Elisabeth Griffith describes

it: “They demanded a pardon for Vaughan, an end to the double standard

of morality, the right of wome n to serve as jurors , and the admission of

women to law schools. . . . According to Stanton, Vaughan’s trial by a

jury of men . . . illustrated the indignity and injustice of women’ s legal

status.”3 6 Vaughan was pardoned. More crucially, her champions and thei r suc­

cessors went on to win for American wome n in general full equality before

the law, including the right to vote, the right to hold property even in

marriage, the right to divorce, and the right to equal education.


The aims of the Seneca Falls activists were clearly stated, finite, and

practicable. They would eventually be realized because they were

grounded in principles—recognized constitutional principles—tha t were

squarely in the tradition of equity, fairness, and individual liberty. Stan­

ton’s reliance on the Declaration of Independenc e was not a ploy; it was

a direct expression of her own sincere creed, and it was the creed of the

assembled men and women. Indeed, it is worth remembering that Seneca

Falls was organized by both me n and wome n and that me n actively

participated in it and were welcomed.3 7 Misandrism (hostility to men, the

counterpar t to misogyny) was not a notable feature of the women’ s move ­

ment unti l our own times.


dafuq, but good it got changed!



Recently several male student s at Vassar were falsely accused of date

rape. After thei r innocence was established, the assistant dean of students ,

Catherine Comins , said of thei r ordeal : “They have a lot of pain, but it is

not a pain that I would necessarily have spared them. I think it ideally

initiates a process of self-exploration. ‘How do I see women?’ ‘If I did not

violate her , could I have?’ ‘Do I have the potential to do to her what they

say I did?’ These are good questions.”8 Dean Comins clearly feels justified

in trumping the commo n law principle “presumed innocent unti l proven

guilty” by a new feminist principle, “guilty even if proven innocent.”

Indeed, she believes that the student s are not really innocent after all.

How so? Because, being male and being brought u p in the patriarchal

culture, they could easily have done wha t they were falsely accused of

having done , even though they didn’ t actually do it. Wher e men are

concerned, Comins quite sincerely believes in collective guilt. Moreover,

she feels she can rely on her audience to be in general agreement with

her on this.





Does it matter that academic feminists speak of replacing seminars

with “ovulars,” history with “herstory,” and theology with “thealogy”?

Should it concern us that mos t teachers of women’ s studies think of

knowledge as a “patriarchal construction”? It should, because twenty

years ago the nation’s academies offered fewer than twenty courses in

women’ s studies; today such courses numbe r in the tens of thousands .

Such rapid growth, which even now shows little signs of abating, is un­

precedented in the annal s of higher education. The feminist coloniza-

tion of the American academy warrants study. Wha t is driving it? Is it a

good thing?


u know, i thought it was a parody when critics said “herstory”. But it wanst!



The misplaced efforts to avoid slighting women lead quickly to exten­

sive “re-visionings” of history, art , and the sciences. The Center for the

Study of Social and Political Change at Smith College did a critical study

of three of the mos t widely used new high school American history

textbooks. Because of state mandates for gender equality, the author s of

the new textbooks had to go out of their way to give wome n prominence.

The Smith researchers were not happy with the results:


There is one major problem .. . in writing nonsexist history text­

books . Most of America’s history is male-dominated, in par t because

in mos t states wome n were not allowed to vote in federal elections

or hold office unti l the twentieth century. This may be regrettable,

but it is still a fact. What , then, is a nonsexist writer of the American

history textbook to do? The answer is filler feminism.1 9


Filler feminism pads history with its own “facts” designed to drive

home the lessons feminists wish to impart . The following passage from

one of the mos t widely used high school American history texts, American

Voices, is a good example of the sort of “feel good” feminist spin that has

become the norm in our nation’s textbooks:


A typical [Indian] family thus consisted of an old woman, her

daughter s with thei r husbands and children, and her unmarried

granddaughter s and grandsons . . . . Politically, women’ s roles and

status varied from culture to culture. Wome n were mor e likely to

assume leadership roles among the agricultural peoples than among

nomadi c hunters . In addition, in many cases in which women did

not become village chiefs, they still exercised substantial political

power . For example, in Iroquois villages, when selected men sat in

a circle to discuss and make decisions, the senior women of the

village stood behind them, lobbying and instructing the men. In

addition, the elder wome n named the male village chiefs to their

positions.2 0


Though some of the information about the Iroquois is vaguely correct ,

the paragraph is blatantly designed to give high school student s the

impression that mos t Native American societies tended to be politically

matriarchal . Since that is not true, the textbook “covers” itself by the

formal disclaimer that “in many cases .. . the wome n did not become

village chiefs.” (In how many cases? A smal l minority? A large majority?)

This is patronizing to both Indians and women , and there is no basis for

it. There are mor e than 350 recognized Indian tribes—one can n o mor e

generalize about them than one can about “humanity. ” Here is wha t

Gilbert Sewall of the American Textbook Counci l says about this passage:

“Female-headed households? Bad old history may cede to bad new his­

tory. The presentist spin on Indian society found in the American Voices

passage is less versed in evidence than aligned to contemporary feminist

politics and perspectives.”2 1



I think the EU recently tried something like this as well, but i cant find the ref.



The problem of “filler feminism” will get worse. Transformationists are

wel l organized, and thei r influence is growing apace. Because of transfor­

mationist pressures , the law in some states now actually mandates “gen­

der-fair” history. The California State Department of Education has issued

guidelines called “Standards for Evaluation of Instructional Materials with

Respect to Social Content. ” According to Education Code section

60040(a) and 60044(a) , “Whenever an instructional material presents

development s in history or current events, or achievements in art, science,

or any other field, the contributions of wome n and men should be rep­

resented in approximately equal number.”2 6 In effect, this law demands

that the historian be mor e attentive to the demands of “equal representa­

tion” than to the historical facts. Needless to say, histories and social

studies presented in this “fair” but factually skewed manne r constitute an

unworthy and dishones t approach to learning.

In the history of the high arts the absence of wome n is deplorable but

largely irreparable. Few wome n in the past were allowed to train and

work in the major arts. Because of this, me n have wrought mos t of the

works that are commonly recognized as masterpieces. But here, espe­

cially, the temptation to redress past wrongs through “reconceptualiza-

tion” has proved irresistible.



In their critique of the imperial male culture, the transformationist

feminists do not confine themselves to impugning the history, art , an d

literature of the past . They also regard logic and rationality as “phallocen-

tric.” Elizabeth Minnich traces the cultural tradition to a “few privileged

males . . . wh o are usually called ‘The Greeks. ‘ “3 4 In common with many

other transformationists, Minnich believes that the conceptions of ratio­

nality and intelligence are white, male creations: “At present . . . not only

are student s taught ‘phallocentric’ and ‘colonial ‘ notions of reason as the

forms of rational expression, but the full possible range of expression of

huma n intelligence also tends to be forced into a severely shrunken no –

tion of intelligence.”3 5 Note the reference to a “colonial” rationality with

its implication of deliberate subjugation. It is now commo n practice to

use scare quotes to indicate the feminist suspicion of a “reality” peculiar

to male ways of knowing. For example, the feminist philosopher Joyce

Trebilcot speaks of “the apparatuses of ‘truth, ‘ ‘knowledge, ‘ ‘science, ‘ ”

that men use to “project their personalities as reality.”3 6


The attack on traditional culture has thus escalated to an attack on the

rational standards and methods that have been the hallmark of scientific

progress. The New Jersey Project for reforming the public schools circu­

lates a document entitled “Feminist Scholarship Guidelines.” The first

guideline is unexceptionable: “Feminist scholars seek to recover the lost

work and thought of wome n in all areas of huma n endeavor.”3 7 But after

that , the guidelines unravel : “Feminist scholarship begins with an aware­

ness that muc h previous scholarship has offered a white, male, Eurocen­

tric, heterosexist , and elite view of’reality. ‘ ”


The guidelines elaborate on the attitude toward masculinist scholarship

and methods by quoting the feminist theorist Elizabeth Fee: “Knowledge

was created as an act of aggression—a passive nature had to be interro­

gated, unclothed, penetrated, and compelled by ma n to reveal her se­

crets.” Fee’s resentment and suspicion of male “ways of knowing” follows

a path wel l trodden by such feminist thinkers as Mary Ellman, Catharine

MacKinnon, and Sandra Harding, whose views of patriarchal knowledge

and science have quickly become central gender feminist doctrine. Play­

ing on the biblical double meaning of knowing to refer both to intercourse

and to cognition, Ellman and MacKinnon claim that men approach nature

as rapists approach a woman , taking joy in violating “her,” in “penetrat ­

ing” her secrets. Feminists, says MacKinnon, have finally realized that for

men, “to know has meant to fuck.”3 8 In a similar mood, Sandra Harding

suggests that Newton’ s Principles of Mechanics could jus t as aptly be

called “Newton’ s Rape Manual.”





Male scholars specializing in their masculinist academic disciplines

(from chemistry to philosophy) are known to transformationists as “sep­

arate knowers. ” The author s of Women’s Ways oj Knowing, a text muc h

cited by transformationists, define “separate knowing” as “the game of

impersonal reason,” a game that has “belonged traditionally to boys.”4 0

“Separate knower s are tough-minded. They are like doormen at exclusive

clubs. They do not want to let anything in unless they are pretty sure it is

good. . . . Presented with a proposition, separate knower s immediately

look for something wrong—a loophole, a factual error, a logical contra­

diction, the omission of contrary evidence.”4 1


Separate knowers—mainly men—pla y the “doubting game. ” The au­

thors of Women’s Ways of Knowing contrast separate knowing with a

higher state of “connected knowing” that they view as the mor e feminine.

In place of the “doubting game,” connected knower s play the “believing

game.” This is more congenial for wome n because “many women find it

easier to believe than to doubt.”4 2


not science!



Linda Gardiner , editor of the Women’s Review of Books, which is housed

in the Wellesley College Center for Research on Women , wonder s

whether Western philosophy speaks for wome n at all. “We might begin

to question the impor t of Descartes’ stress on logic and mathematics as

the ideal types of rationality, in a society in which only a tiny percentage

of people could realistically spend time developing skills in those fields,”

she writes.5 9 Noting that the philosophical elite is biased in favor of the

abstract , methodical , and universal , Gardiner suggests that a feminist

philosophy would be mor e concrete and mor e suspicious of logic and

method. “What would a female logic be like?” she asks, and answer s that

this would be like asking wha t female astronomy or particle physics

would be like. “We cannot imagine wha t it would mean to have a ‘female

version’ of them.”6 0 For that , says Ms. Gardiner , we should first need to

develop different epistemologies. Reading Gardiner’s spirited argument s

for the thesis that classical philosophy is essentially and inveterately male

biased, one cannot avoid the impression that the feminist critic is mor e

ingenious at finding male bias in a field than in proposing an intelligible

alternative way to deal with its subject matter .


Reminds me of:


“You can buy any number of books on ‘quantum healing’, not to mention quantum psychology, quantum responsibility, quantum morality, quantum aesthetics, quantum immortality and quantum theology. I haven’t found a book on quantum feminism, quantum financial management or Afro-quantum theory, but give it time.”
– Richard Dawkins, A Devil’s Chaplain (Page 147)


Just replace “quantum” by ”feminist” and u apparently can get “feminist particle physics” “feminist astronomy” and “feminist logic”.


What the fuck am i reading



Feminist critics have looked at the metaphor s of “male science” and

found them sexist. I recently heard a feminist astronomer interviewed on

CNN say in all seriousness that sexist terminology like “the Big Bang

Theory” is “off-putting to young women ” wh o might otherwise be inter­

ested in pursuing careers in her field.64 It is hard to believe that anyone

with an intelligent interest in astronomy would be pu t off by a graphic

description of a cosmic event . Othe r critiques of science as masculinist

are equally fatuous and scientifically fruitless. After asserting that “the

warlike terminology of immunology which focuses on ‘competition, ‘ ‘in­

hibition, ‘ and ‘invasion’ as major theories of how cells interact reflects a

militaristic view of the world, ” Sue Rosser, wh o offers workshops on how

to transform the biology curriculum, concedes that “a feminist critique

has not yet produced theoretical changes in the area of cell biology.”6 5

She does not tell us how the “feminist critique” could lead to advances in

biology, but she considers it obvious that it must : “It becomes evident

that the inclusion of a feminist perspective leads to changes in models,

experimental subjects, and interpretations of the data. These changes

entai l mor e inclusive, enriched theories compared to the traditional , re­

strictive, unicausal theories.”6 6



Yet although the transformationists have every reason to celebrate thei r

many successes, they have recently experienced a setback from an unex­

pected quarter . Whe n Mcintosh, Minnich, and thei r followers demande d

that the oppressive European, white, male culture being taught in the

schools be radically transformed, they had not imagined that anyone

could look upo n them as oppressors. The transformationist leaders are

not men, but they are white, they are “European,” they are middle-class.

Minority wome n have begun to deny that the leaders of the women’ s

movement have any right to speak for them. Most member s of the wome n

of color caucus boycotted the 1992 Austin National Women’ s Studies

Conference I attended for its failure to recognize and respect their political

identity. The slighted group sent the conferees an African-American wom­

en’s quil t made from dashiki fabrics, as both a reprimand and a “healing

gesture.” The assembled white feminists sat before it in resentful but

guilty silence. In the game of moral one-upmanship that gender feminists

are so good at, they had been outquilted, as it were, by a mor e marginal ­

ized constituency. Clearly any number of minority groups can play the

victimology game, and almost all could play it far mor e plausibly than

the socially well-positioned Heilbruns, Mclntoshes, and Minniches.


Hahahahaha! Pwned at their own game.



Women: A Feminist Perspective is said to be the best-selling women’ s

studies textbook of all time. The first selection, “Sexual Terrorism” by

Carole J. Sheffield, is a good example of how the feminist classroom can

“infuse” anxiety and rage. Ms. Sheffield describes an “ordinary” event that

took place early one evening whe n she was alone in a Laundromat : “The

laundroma t was brightly lit; and my car was the only one in the lot.

Anyone passing by could readily see that I was alone and isolated. Know­

ing that rape is a crime of opportunity, I became terrified.” Ms. Sheffield

left her laundry in the washer and dashed back to her car, sitting in it

with the door s locked and the windows up. “When the wash was com­

pleted, I dashed in, threw the clothes into the drier, and ran back out to

my car. Whe n the clothes were dry, I tossed them recklessly into the

basket and hurriedly drove away to fold them in the security of my home.

Although I was not victimized in a direct , physical way or by objective or

measurable standards , I felt victimized. It was, for me, a terrifying expe­

rience.” At home , her terror subsides and turns to anger: “Mostly I was

angry at being unfree: a hostage of a culture that , for the mos t part ,

encourages violence against females, instructs men in the methodology of

sexual violence, and provides them with ready justification for their vio­

lence. . . . Following my experience at the Laundromat , I talked with my

student s about terrorization.”





For the pas t few years I have reviewed hundreds of syllabi from wom­

en’s studies courses, attended mor e feminist conferences than I care to

remember , studied the new “feminist pedagogy,” reviewed dozens of

texts, journals , newsletters, and done a lot of late-into-the-night reading

of e-mai l letters that thousands of “networked” women’ s studies teachers

send to one another . I have taught feminist theory. I have debated gender

feminists on college campuses around the country, and on national tele­

vision an d radio. My experience with academic feminism and my immer ­

sion in the ever-growing gender feminist literature have served to deepen

my conviction that the majority of women’ s studies classes and other

classes that teach a “reconceptualized” subject matter are unscholarly,

intolerant of dissent , and full of gimmicks. In other words , they are a

waste of time. And although they attract female student s because of their

social ambience, they attract almost no men. They divert the energies of

students—especially young women—wh o sorely need to be learning

how to live in a world that demand s of them applicable talents and skills,

not feminist fervor or ideological rectitude.


In other words, a feminist argument for why feminism as a field is bad.



The feminist classroom does little to prepare student s to cope in the

world of work and culture. It is an embarrassing scandal that , in the name

of feminism, young wome n in our colleges and universities are taking

courses in feminist classrooms that subject them to a lot of bad prose,

psychobabble, and “new age” nonsense. Wha t has real feminism to do

with sitting around in circles and talking about our feelings on menstrua­

tion? To use a phrase muc h used by resenter feminists, the feminist

classroom shortchanges wome n students . It wastes their time and gives

them bad intellectual habits. It isolates them, socially and academically.

While male student s are off studying such “vertical” subjects as engineer ­

ing and biology, wome n in feminist classrooms are sitting around being

“safe” and “honoring” feelings. In this way, gender feminist pedagogy

plays into old sexist stereotypes that extol women’ s capacity for intuition,

emotion, and empathy while denigrating their capacity to think objec­

tively and systematically in the way me n can.


A parent should think very carefully before sending a daughter to one

of the mor e gender-feminized colleges. Any school has the freedom to

transform itself into a feminist bastion, but because the effect on the

students is so powerful it ought to be hones t about its attitude. I would

like to see Wellesley College, Mount Holyoke, Smith, Mills, and the

University of Minnesota—among the mor e extreme examples—print the

following announcement on the first page of their bulletins:


We wil l help your daughter discover the extent to which she has

been in complicity with the patriarchy. We will encourage her to

reconstruct herself through dialogue with us. She may become en­

raged and chronically offended. She will very likely reject the reli­

gious and moral codes you raised her with. She may wel l distance

herself from family and friends. She may change her appearance,

and even her sexual orientation. She may end u p hating you (her

father) and pitying you (her mother) . After she has completed her

reeducation with us , you will certainly be out tens of thousands of

dollars and very possibly be out one daughter as well .


At the Austin conference, my sister and I attended a packed worksho p

called “White Male Hostility in the Feminist Classroom,” led by two

female assistant professors from the State University of New York at

Plattsburgh. What to do about young me n wh o refuse to use gender –

neutral pronouns? Most agreed that the instructor should grade them

down. One of the Plattsburghers told us about a male student wh o had

“baited her” whe n she had defended a fifteen-year-old’s right to have an

abortion without parental consent . The student had asked, “What about

a 15-year-old that wanted to marry a 30-year-old?” She referred to this as

a “trap.” In philosophy, it is known as a legitimate counterexample to be

treated seriously and deal t with by counterargument . But she wanted to

know wha t advice we had to offer.


Haha, well played! If 15 year olds are to have the freedom to get abortions, why shud they not likewise get the freedom to date much older men? Which is the more dangerous?



The claim that all teaching is a form of indoctrination, usually in the

service of those wh o are politically dominant , helps to justify the peda­

gogy of the feminist classroom. Feminist academics often say that apar t

from the enclave of women’ s studies, the university curriculum consists

of “men’ s studies.” They mean by this that mos t of what student s normally

learn is designed to maintain and reinforce the existing patriarchy. To

anyone wh o actually believes this, combatting the standard indoctrination

with a feminist “counter-indoctrination” seems only fair and sensible.


The British philosopher Roger Scruton, aided by two colleagues at the

Education Research Center in England, has pointed to several prominent

features that distinguish indoctrination from normal education.1 8 In a

competent , well-designed course, student s learn methods for weighing

evidence and critical methods for evaluating argument s for soundness .

They learn how to arrive at reasoned conclusions from the best evidence

at hand. By contrast , in cases of indoctrination, the conclusions are as­

sumed beforehand. Scruton calls this feature of indoctrination the “Fore­

gone Conclusion.” According to Scruton, the adoption of a foregone

conclusion is the mos t salient feature of indoctrination. In the case of

gender feminism, the “foregone conclusion” is that American men strive

to keep wome n subjugated.



In December 1989 I received a phon e call from a ma n wh o told me he

was a graduate student at the University of Minnesota. He asked me to

look into some “frightening” things campus feminists were u p to. He

mentioned the Scandinavian studies department . He told me he did not

want to give me his name because he felt he would be hurt : “They are

powerful , they are organized, and they are vindictive.”



Having heard “both sides” of the feminist question at Minnesota, I felt

ready to tackle the mystery of the Scandinavian studies department . It

turned out not to be a mystery at all—only a disturbing example of

extreme feminist vigilance.


On Apri l 12, 1989, four female graduate student s filed sexual harass­

men t charges against all six tenured member s of the Scandinavian studies

department (five me n and one woman) . The professors were called to

Dean Fred Lukerman’ s office, notified of the charges and, according to

the accused, told they’d better get themselves lawyers.


In a letter sent to Professor William Mischler of Scandinavian studies,

Ms. Patricia Mullen, the university officer for sexual harassment , informed

Mischler that he had been accused of sexual harassment and would be

reported to the provos t unless he responded within ten days. Similar

letters were sent to the other five professors. Mischler’s letters contained

no specific facts that could be remotely considered to describe sexual

harassment . Whe n Mischler made further inquiries, he discovered he had

been accused of giving a narrow and “patriarchal” interpretation of Isaak

Dinesen’s work, of not having read a novel a student deemed important ,

and of having greeted a student in a less than friendly manner . Two of

Mischler’s colleagues were accused of harassing the plaintiffs by not hav­

ing given them higher grades.


The plaintiffs had drawn u p a list of punitive demands , among them:

1. the denial of meri t pay for a period of not less than five years;

2. monthly sexual harassment workshops for all Scandinavian core

faculty for at least twelve months ; and

3. annual sexual harassment workshops for all Scandinavian core fac­

ulty, adjunct faculty, visiting faculty, graduate assistants, reader –

graders, and graduate students .


Lacking any suppor t from the administration whatsoever , the profes­

sors were forced to seek legal counsel . On October 13, six month s later,

all charges against four of the accused were dropped. No explanation was

offered. A few month s later, the charges against the remaining two were

dropped, again without explanation. All of them are still shaken from

what they describe as a Kafkaesque ordeal . “When I saw the charges,”

says Professor Allen Simpson, “I panicked. It’s the mos t terrifying

thing . . . they want me fired. It cost me two thousand dollars to have my

response drafted. I can’ t afford justice.”


Professor Mischler requested that the contents of the complaint s be

made public to the Minnesota community. But, according to the Minne­

sota Daily, Patricia Mullen opposed disclosure on the grounds that “it

would dampe n people from coming forward.”4 5


My efforts to reach someone wh o could give me the administration’s

side of the story were not successful. Ms. Mullen declined to speak with

me. Fred Lukerman, wh o was dean of the College of Liberal Arts at the

time, also proved to be inaccessible. I finally did talk to a dean wh o

assured me he was very supportive of feminist causes on campus , but that

he believed the Scandinavian studies affair was indeed a “witch hunt. ”

“But please do not use my name, ” he implored.



In math, at least, it appear s that the vaunted correlation between self-

esteem and achievement does not hold. Instead of a bill called “Gender

Equity in Education,” we need a bill called “Commo n Sense in Educa­

tion,” which would oversee the way the government spends money on

phony education issues. The measure would not need a very big budget ,

but it could save millions by cutting out unneeded projects like the ones

proposed for raising self-esteem and force us instead to address directly

the very real problems we mus t solve if we are to give our student s the

academic competence they need and to which they are entitled.



Paglia’s dismissal of date rape hype infuriates campus feminists, for

whom the rape crisis is very real. On mos t campuses, date-rape groups

hold meetings, marches , rallies. Victims are “survivors,” and their friends

are “co-survivors” wh o also suffer and need counseling.4 1 At some rape

awareness meetings , wome n wh o have not yet been date raped are re­

ferred to as “potential survivors.” Thei r male classmates are “potential

rapists.”4 2





In The Morning After, Katie Roiphe describes the elaborate measures

taken to prevent sexual assaults at Princeton. Blue lights have been in­

stalled around the campus , freshman wome n are issued whistles at ori­

entation. There are marches , rape counseling sessions, emergency

telephones. But as Roiphe tells it, Princeton is a very safe town, and

whenever she walked across a deserted golf course to get to classes, she

was mor e afraid of the wild geese than of a rapist . Roiphe reports that

between 1982 and 1993 only two rapes were reported to the campus

police. And, whe n it comes to violent attacks in general , male student s

are actually mor e likely to be the victims. Roiphe sees the campus rape

crisis movement as a phenomeno n of privilege: these young wome n have

had it all, and whe n they find out that the world can be dangerous and

unpredictable, they are outraged:



Othe r critics, such as Camille Paglia and Berkeley professor of social

welfare Nei l Gilbert , have been targeted for demonstrations, boycotts, and

denunciations . Gilbert began to publish his critical analyses of the Ms./

Koss study in 1990.5 7 Many feminist activists did not look kindly on

Gilbert’s challenge to thei r “one in four” figure. A date rape clearinghouse

in San Francisco devotes itself to “refuting” Gilbert ; it sends out masses

of literature attacking him. It advertises at feminist conferences with green

and orange fliers bearing the headline STOP IT, BITCH! The words are not

Gilbert’s, but the tactic is an effective way of drawing attention to his

work. At one demonstration against Gilbert on the Berkeley campus ,

student s chanted, “Cut it out or cut it off,” and carried signs that read,

KILL NEIL GILBERT! 5 8 Sheila Kuehl , the director of the California Women’ s

Law Center , confided to readers of the Los Angeles Daily Journal, “I found

myself wishing that Gilbert , himself, might be raped and .. . be told, to

his face, it had never happened.”


That’s so extreme it probably was illegal.



Betty Friedan once told Simone de Beauvoir that she believed women

should have the choice to stay home to raise their children if that is what

they wish to do. Beauvoir answered: “No, we don’ t believe that any

woma n should have this choice. No woma n should be authorized to stay

at home to raise her children. Society should be totally different. Wome n

should not have that choice, precisely because if there is such a choice,

too many wome n will make that one.”4


The totalitarianism shines thru once again.



I can’ t help being amused by how upset the New Feminists get over

the vicarious pleasure wome n take in Scarlett’s transports. All that incor­

rect swooning! How are we ever going to get wome n to see how wrong it

is? Nevertheless, the gender feminists seem to believe that thirty years

from now, with the academy transformed and the feminist consciousness

of the population raised, there will be a new Zeitgeist. Wome n who

interpret sexual domination as pleasurable will then be few and far be­

tween, and Scarlett, alas, will be out of style.


Is this scenario out of the question? I think it is. Sexuality has always

been par t of our natures , and there is no one right way. Men like Rhet t

Butler wil l continue to fascinate many women. Nor will the doctrine that

this demeans them have muc h of an effect. How many women who like

Rhet t Butler-type s are in search of suppor t groups to help them change?

Such wome n are not grateful to the gender feminists for going to war

against male lust . They may even be offended at the suggestion that they

themselves are being degraded and humiliated; for that treats their enjoy­

ment as pathological .



So far, the efforts to get wome n to overhaul their fantasies and desires

have been noncoercive, but they do not seem to have been particularly

effective. To get the results they want , the gender feminists have turned

thei r attention to ar t and literature, wher e fantasies are manufactured and

reinforced. Ms. Friedman calls our attention to Angela Carter’s feminist

rewrite of the “morning after” scene in Gone with the Wind: “Scarlett lies

in bed smiling the next morning because she broke Rhett’s kneecaps the

night before. And the reason that he disappeared before she awoke was

to go off to Europe to visit a good kneecap specialist.”3 0


This is meant to be amusing, but of course the point is serious. For the

gender feminist believes that Margaret Mitchel l got it wrong. If Mitchell

had understood better how to make a true heroine of Scarlett, she would

have mad e her different. Scarlett would then have been the kind of person

wh o would plainly see that Rhet t mus t be severely punished for what he

had inflicted on he r the night before. More generally, the gender feminist

believes she mus t rebut and replace the fiction that glorifies dominant

males and the wome n wh o find them attractive. This popular literature,

which “eroticizes” male dominance , mus t be opposed and, if possible,

eradicated. Furthermore , the feminist establishment mus t seek ways to

foster the popularity of a new genre of romantic film and fiction that

sends a mor e edifying message to the wome n and men of America. A

widely used textbook gives us a fair idea of what that message should be:


Plots for nonsexist films could include wome n in traditionally male

jobs (e.g. , long-distance truck driver). . . . For example, a high-

ranking female Army officer, treated with respect by men and

wome n alike, could be shown not only in various sexual encounters

with other people but also carrying out her job in a human e manner .

Or perhaps the main character could be a female urologist . She

could interact with nurses and other medical personnel , diagnose

illnesses brilliantly, and treat patients with great sympathy as wel l

as have sex with them. Whe n the Army officer or the urologist

engage in sexual activities, they will treat their partners and be

treated by them in some of the considerate ways described above.3 1


The truck driver and the urologist are meant to be serious role models

for the free feminist woman , humane , forthrightly sexual , but not discrim­

inating against either gender in her preferences for partners, so consider­

ate that all wil l respect her . These model s are projected in the hope that

someday films and novels with such themes and heroines will be pre ­

ferred, replacing the currently popula r “incorrect” romances with a mor e

acceptable ideal .


It seems a futile hope . Perhaps the best way to see wha t the gender

feminists are u p against is to compare their version of romance with that

embodied in contemporary romance fiction that sells in the millions. Here

is a typical example:


Townsfolk called him devil. For dark and enigmatic Julian, Earl of

Ravenwood, was a ma n with a legendary temper and a first wife

whose mysterious death would not be forgotten. . . . Now country-

bred Sophy Dorring is about to become Ravenwood’s new bride.

Drawn to his masculine strength and the glitter of desire that burned

in his emerald eyes, the tawny-haired lass had her own reasons for

agreeing to a marriage of convenience. . . . Sophy Dorring intended

to teach the devi l to love.3 2


Romance novels amoun t to almost 4 0 percent of all mass-market pa­

perback sales. Harlequin Enterprises alone has sales of close to 200 mil ­

lion books worldwide. They appear in many languages, including

Japanese, Swedish, and Greek, and they are now beginning to appear in

Eastern Europe. The readership is almost exclusively women.3 3 The chal­

lenge this present s to gender feminist ideologues is mos t formidable since

almost every hero in this fictional genre is an “alpha male” like Rhet t

Butler or the Earl of Ravenwood. It was therefore to be expected that the

New Feminists would make a concerted attempt to correct this literature

and to replace it by a new one.



Data are based on GRE averages. Data from here:

I have sorted them for Average-SDU, i.e. estimated g (intelligence). As a funny note, top 10 includes 5 different physics fields. #11 is also physics…

Data file here: IQ data by academic field GRE

Illustrations from the same source.

Field V-mean M-mean V-SDU M-SDU Average-SDU Difference-SDU
Physics 540 743 0.66 1.05 0.85 -0.4
Classical Language 619 633 1.32 0.32 0.82 0.99
History of Science 596 661 1.13 0.51 0.82 0.62
Astrophysics 540 727 0.66 0.95 0.8 -0.29
Mathematics 523 740 0.51 1.03 0.77 -0.52
Atomic Physics 522 739 0.5 1.03 0.77 -0.52
Solid State Physics 514 743 0.44 1.05 0.74 -0.62
Biophysics 523 727 0.51 0.95 0.73 -0.43
Classics 609 616 1.24 0.21 0.72 1.02
Planetary Science 545 694 0.7 0.73 0.71 -0.03
Physics, Other 519 723 0.48 0.92 0.7 -0.44
Philosophy 591 630 1.08 0.3 0.69 0.78
Astronomy 525 706 0.53 0.81 0.67 -0.28
Materials Science 509 728 0.39 0.95 0.67 -0.56
Chemistry, Physical 513 708 0.43 0.82 0.62 -0.39
Nuclear Physics 506 715 0.37 0.87 0.62 -0.5
Optics 495 729 0.28 0.96 0.62 -0.68
Aerospace Engineering 498 725 0.3 0.93 0.62 -0.63
Biomedical Engineering 504 717 0.35 0.88 0.62 -0.53
Operations Research 483 743 0.18 1.05 0.61 -0.88
Nuclear Engineering 500 720 0.32 0.9 0.61 -0.58
Chemical Engineering 490 729 0.24 0.96 0.6 -0.72
Economics 508 707 0.39 0.81 0.6 -0.43
Russian 584 611 1.03 0.18 0.6 0.85
Applied Math 487 730 0.21 0.97 0.59 -0.76
Linguistics 566 630 0.87 0.3 0.59 0.57
Probability & Stats 486 728 0.2 0.95 0.58 -0.75
Neuroscience 533 665 0.6 0.54 0.57 0.06
Comp Lit 591 588 1.08 0.03 0.56 1.06
Chemistry, Inorganic 502 690 0.34 0.7 0.52 -0.37
Ecology 535 638 0.61 0.36 0.49 0.26
Mechanical Engineering 471 721 0.08 0.91 0.49 -0.83
Math, Other 474 715 0.1 0.87 0.48 -0.77
Electrical Engineering 465 722 0.03 0.91 0.47 -0.89
Finance 466 721 0.03 0.91 0.47 -0.87
Geochemistry 514 657 0.44 0.48 0.46 -0.05
Actuarial Science 460 726 -0.02 0.94 0.46 -0.96
Chemistry, Organic 490 683 0.24 0.66 0.45 -0.42
Computer Engineering 465 716 0.03 0.87 0.45 -0.85
Cognitive Psychology 532 627 0.59 0.28 0.44 0.3
Chemistry, Gen 483 681 0.18 0.64 0.41 -0.47
Atmospheric Science 490 673 0.24 0.59 0.41 -0.35
Geophysics 487 676 0.21 0.61 0.41 -0.4
Paleontology 531 621 0.58 0.25 0.41 0.33
Cell & Mol Bio 497 658 0.29 0.49 0.39 -0.2
Biochemistry 486 669 0.2 0.56 0.38 -0.36
Immunology 492 662 0.25 0.52 0.38 -0.26
Civil Engineering 456 705 -0.05 0.8 0.38 -0.85
Quantitative Psychology 515 629 0.45 0.3 0.37 0.15
Genetics 496 651 0.29 0.44 0.36 -0.16
Botany 513 626 0.43 0.28 0.35 0.15
Computer Science 453 702 -0.08 0.78 0.35 -0.86
Music History 536 596 0.62 0.08 0.35 0.54
Chemistry, Other 477 659 0.13 0.5 0.31 -0.37
Theology 537 583 0.63 -0.01 0.31 0.64
Meteology 470 663 0.07 0.52 0.3 -0.46
International Relations 531 588 0.58 0.03 0.3 0.55
European History 554 555 0.77 -0.19 0.29 0.97
Geology 495 625 0.28 0.27 0.27 0.01
Social Psychology 518 594 0.47 0.07 0.27 0.4
Zoology 505 609 0.36 0.17 0.26 0.2
Develop Bio 490 623 0.24 0.26 0.25 -0.02
Entomology 505 606 0.36 0.15 0.25 0.22
Marine Biology 499 611 0.31 0.18 0.24 0.13
Creative Writing 553 540 0.76 -0.29 0.24 1.06
Chemistry, Analytical 464 652 0.02 0.45 0.23 -0.43
Environ Science 493 615 0.26 0.21 0.23 0.06
Industrial Engineering 426 699 -0.3 0.76 0.23 -1.06
Anthropology 532 562 0.59 -0.15 0.22 0.73
Political Science 523 574 0.51 -0.07 0.22 0.58
Art history 536 549 0.62 -0.23 0.2 0.85
Microbiology 482 615 0.17 0.21 0.19 -0.04
Other Biology 473 626 0.09 0.28 0.19 -0.19
Epidemiology 485 610 0.19 0.17 0.18 0.02
American History 533 541 0.6 -0.28 0.16 0.88
Biology 477 606 0.13 0.15 0.14 -0.02
Toxicology 465 610 0.03 0.17 0.1 -0.15
Petroleum Engineering 414 676 -0.4 0.61 0.1 -1.01
Computer Programming 407 681 -0.46 0.64 0.09 -1.1
Physiology 464 606 0.02 0.15 0.08 -0.13
Drama 514 541 0.44 -0.28 0.08 0.72
Chemistry, Pharm 429 647 -0.28 0.42 0.07 -0.69
Secondary Education 484 576 0.18 -0.05 0.07 0.24
Pathology 468 594 0.05 0.07 0.06 -0.02
Information Science 446 621 -0.13 0.25 0.06 -0.38
Pharmacology 429 634 -0.28 0.33 0.03 -0.61
Music History 490 559 0.24 -0.17 0.03 0.4
Clinical Psychology 484 554 0.18 -0.2 -0.01 0.38
Developmental Psychology 476 563 0.12 -0.14 -0.01 0.26
Sociology 490 541 0.24 -0.28 -0.02 0.52
Psychology 476 546 0.12 -0.25 -0.07 0.37
Anatomy 443 568 -0.16 -0.11 -0.13 -0.05
Business Adminstraiton 434 570 -0.24 -0.09 -0.16 -0.14
Nursing 452 531 -0.08 -0.35 -0.22 0.27
Communication 458 517 -0.03 -0.44 -0.24 0.41
Nutrition 432 542 -0.25 -0.28 -0.27 0.03
Elementary Education 438 520 -0.2 -0.42 -0.31 0.22
Counseling Psychology 444 500 -0.15 -0.56 -0.35 0.41
Community Psychology 441 493 -0.18 -0.6 -0.39 0.43
Special Education 424 497 -0.32 -0.58 -0.45 0.26
Early Childhood Education 420 497 -0.35 -0.58 -0.46 0.22
Criminal Justice/Criminology 418 477 -0.37 -0.71 -0.54 0.34
Social Work 428 463 -0.29 -0.8 -0.54 0.52
Physical Education 389 487 -0.61 -0.64 -0.63 0.03

The One World Schoolhouse – Salman Khan ebook free download pdf

This is a short, easy to read, nonacademic (few references) book. it has som shortcomings on matters dealing with test taking and intelligence tests, but isnt that important for the main topics of the book. this book shud be read by anyone interested in public policy regarding education.



As always, quotes and comments below. quotes ar in red.




I was born in Metairie, Louisiana, a residential area within

metro New Orleans. My father, a pediatrician, had moved

there from Bangladesh for his medical residency at LSU and,

later, his practice at Charity Hospital. In 1972, he briefly

returned to Bangladesh and came back with my mother—who

was born in India. It was an arranged marriage, very traditional

(my mother tried to peek during the ceremony to make sure

she was marrying the brother she thought she was). Over the

next several years, five of my mother’s brothers and one cousin

came to visit, and they all fell in love with the New Orleans

area. I believe that they did this because Louisiana was as close

to South Asia as the United States could get; it had spicy food,

humidity, giant cockroaches, and a corrupt government. We

were a close family—even though, at any given moment, half

of my relatives weren’t speaking to the other half.






Let me be clear—I think it’s essential for everything that

follows—that at the start this was all an experiment, an impro­

visation. I ’d had no teacher training, no Big Idea for the most

effective way to teach. I did feel that I understood math intu­

itively and holistically, but this was no guarantee that I ’d be

effective as a teacher. I ’d had plenty of professors who knew

their subject cold but simply weren’t very good at sharing what

they knew. I believed, and still believe, that teaching is a sepa­

rate skill—in fact, an art that is creative, intuitive, and highly



i think he is right about that. so, it makes no sens to me when danish politicians focus on having research-based education. this means that the teacher must be a researcher himself. but given the nonperfect and perhaps low (?) correlation between teaching ability and researcher ability, that seems like at best at bad idea, and at worst, a dangerusly bad idea.



It ignores several basic facts about how people actually learn.

People learn at different rates. Some people seem to catch on to

things in quick bursts of intuition; others grunt and grind their

way toward comprehension. Quicker isn’t necessarily smarter

and slower definitely isn’t dumber. Further, catching on quickly

isn’t the same as understanding thoroughly. So the pace of

learning is a question of style, not relative intelligence. The tor­

toise may very well end up with more knowledge—more use­

ful, lasting knowledge—than the hare.


it pains me to read stuff like this. u gotta into g mr. Khan.



Let me emphasize this difference, because it is central to

everything I argue for in this book. In a traditional academic

model, the time allotted to learn something is fixed while the

comprehension of the concept is variable. Washburne was

advocating the opposite. What should be fixed is a high level

of comprehension and what should be variable is the amount of

time students have to understand a concept.


obvius, but apparently ignored by those that support the current one-size fits all system (based on age). well almost one size. ther is special education for those simply too stupid or too unruly or too handicapped to learn somthing in a normal class.



The findings of Kandel and other neuroscientists have much

to say about how we actually learn; unfortunately, the standard

classroom model tends to ignore or even to fly in the face of these

fundamental biological truths. Stressing passivity over activity is

one such misstep. Another, equally important, is the failure of

standard education to maximize the brain’s capacity for associa­

tive learning—the achieving of deeper comprehension and more

durable memory by relating something newly learned to some­

thing already known. Let’s take a moment to consider this.


yes, this is very important. hence why mem-based learning works really well (an online learning site,, is based on this idea, and it works very well!). also think of how memory techniqs work – they ar based on associations as well. cf.


recently, quite a few books hav been written on this subject. probably becus of the recent interest in memory as a sport disciplin. cf.



Active learning, owned learning, also begins with giving

each student the freedom to determine where and when the

learning will occur. This is the beauty of the Internet and the

personal computer. I f someone wants to study the quadratic

equation on his back porch at 3 a.m., he can. I f someone thinks

best in a coffee shop or on the sideline of a soccer field, no prob­

lem. Haven’t we all come across kids who seem bright and alert

except when they’re in class? Isn’t it clear that there are morning

people and night people? The radical portability of Internet-

based education allows students to learn in accordance with

their own personal rhythms, and therefore most efficiently.


good application to fix the morningness vs. eveningsness problem (in DA: a-menneske vs. b-mennesker). cf. and



Tests say little or nothing about a student’s potential to learn

a subject. At best, they offer a snapshot of where the student

stands at a given moment in time. Since we have seen that stu­

dents learn at widely varying rates, and that catching on faster

does not necessarily imply understanding more deeply, how

meaningful are these isolated snapshots?


yes they do. achievement tests correlate well with g factor.



And all of this might have happened because of one snapshot

test, administered on one morning in the life of a twelve-year-

old girl—a test that didn’t even test what it purported to be

testing! The exam, remember, claimed to be measuring math

potential—that is, future performance. Nadia did poorly on it

because of one past concept that she’d misunderstood. She has

cruised through every math class she’s ever taken since (she

took calculus as a sophomore in high school). What does this

say about the meaningfulness and reliability of the test? Yet

we look to exams like this to make crucial, often irreversible,

and deceptively “objective” decisions regarding the futures of

our kids.


it implies that it isnt a perfectly valid test. no one claims that such tests hav perfect validity.


it doesnt say anything about reliability afaict.



What will make this goal attainable is the enlightened use of technology. Let me stress ENLIGHTENED use. Clearly, I believe that technology-enhanced teaching and learning is our best chance for an affordable and equitable educational future. But the key question is how the technology is used. It’s not enough to put a bunch of computers and smartboards into classrooms. The idea is to integrate the technology into how we teach and learn; without meaningful and imaginative integration, technology in the classroom could turn out to be just one more very expensive gimmick.


[had to type it off, apparently, the OCR cudnt handle bold text???]


Surely mr. Khan is right about this.



I happen to believe that every student, given the tools and

the help that he or she needs, can reach this level of profi­

ciency in basic math and science. I also believe it is a disservice

to allow students to advance without this level of proficiency,

because they’ll fall on their faces sometime later.


living in a dream world. good luck teaching math to the mentally retarded.

lesson: this is why NOT to use words like <every> and <all>. it is not possible to raise everybody to full mastery of basic math and science. but it is surely possible to lift most people to new heights with better teaching etc.



It turned out that Peninsula Bridge used the video lessons

and software at three of its campuses that summer. Some of

the ground rules were clear. The Academy would be used in

addition to, not in place of, a traditional math curriculum. The

videos would only be used during “computer time,” a slot that

was shared with learning other tools such as Adobe Photoshop

and Illustrator. Even within this structure, however, there were

some important decisions to be made; the decisions, in turn,

transformed the Peninsula Bridge experience into a fascinating

and in some ways surprising test case.


The first decision was the question of where in math the kids

should start. The Academy math curriculum began, literally,

with 1 + 1=2. But the campers were mainly sixth to eighth

graders. True, most of them had serious gaps in their under­

standing of math and many were working below their grade

level. Still, wouldn’t it be a bit insulting and a waste of time to

start them with basic addition? I thought so, and so I proposed

beginning at what would normally be considered fifth-grade

material, in order to allow for some review. To my surprise,

however, two of the three teachers who were actually imple­

menting the plan said they preferred to start at the very begin­

ning. Since the classes had been randomly chosen, we thereby

ended up with a small but classic controlled experiment.


The first assumption to be challenged was that middle-

school students would find basic arithmetic far too easy. Among

the groups that had started with 1 + 1, most of the kids, as

expected, rocketed through the early concepts. But some didn’t.

A few got stuck on things as fundamental as two-digit subtrac­

tion problems. Some had clearly never learned their multiplica­

tion tables. Others were lacking basic skills regarding fractions

or division. I stress that these were motivated and intelligent

kids. But for whatever reason, the Swiss cheese gaps in their

learning had started creeping in at a distressingly early stage,

and until those gaps were repaired they had little chance of

mastering algebra and beyond.


The good news, however, is that once identified, those gaps

could be repaired, and that when the shaky foundation had been

rebuilt, the kids were able to advance quite smoothly.


This was in vivid and unexpected contrast to the group that

had started at the fifth-grade level. Since they’d begun with

such a big head start, I assumed that by the end of the six-week

program they would be working on far more advanced con­

cepts than the other group. In fact just the opposite happened.

As in the classic story of the tortoise and the hare, the 1 + 1

group plodded and plodded and eventually passed them right

by. Some of the students in the “head start” group, on the other

hand, hit a wall and just couldn’t seem to progress. There were

sixth- and seventh-grade concepts that they simply couldn’t

seem to master, presumably because of gaps in earlier concepts.

In comparing the performance of the two groups, the conclu­

sion seemed abundantly clear: Nearly all the students needed

some degree of remediation, and the time spent on finding and

fixing the gaps turned out both to save time and deepen learning in

the longer term.


if that is really true, thats a HUGELY important finding. any replications of this?



As we settled into the MIT routine, Shantanu and I began

independently to arrive at the same subversive but increasingly

obvious conclusion: The giant lecture classes were a monu­

mental waste of time. Three hundred students crammed into

a stifling lecture hall; one professor droning through a talk he

knew by heart and had delivered a hundred times before. The

sixty-minute talks were bad enough; the ninety-minute talks

were torture. What was the point? Was this education or an

endurance contest? Was anybody actually learning anything?

Why did students show up at all? Shantanu and I came up with

two basic theories about this. Kids went to the lectures either

because their parents were paying x number of dollars per, or

because many of the lecturers were academic celebrities, so

there was an element of show business involved.


i feel exactly the same about my university classes. i want to learn goddamit, not sit in class waiting for it to end.



Then there are the standardized tests to which students are

subjected from third grade straight on through to grad school.

As I ’ve said, I am not antitesting; I believe that well-conceived,

well-designed, and fairly administered tests constitute one of

our few real sources of reliable and relatively objective data

regarding students’ preparedness. But note that I say prepared­

ness, not potential. Well-designed tests can give a pretty solid

idea of what a student has learned, but only a very approximate

picture of what she can learn. To put it in a slightly different

way, tests tend to measure quantities of information (and some­

times knowledge) rather than quality of minds—not to men­

tion character. Besides, for all their attempts to appear precise

and comprehensive, test scores seldom identify truly notable

ability. I f you’re the admissions director at Caltech or in charge

of hiring engineers at Apple, you’re going to see a heck of a lot

of candidates who had perfect scores on their math SATs. They

are all going to be fairly smart people, but the scores tell you

little about who is truly unique.


mr. Khan obvisuly knows little about intelligence tests. sure, SAT, ACT, GRE tests are achievement tests, but those correlate moderately to strongly with g factor, so they are okay to decent intelligence tests. and ofc, IQ tests like RPM are really good at measuring g factor. they really can measure a students potential, in that it measures the students ability very well, and that is closely related to the students potential.



For me personally, the biggest discovery has been how hun­

gry students are for real understanding. I sometimes get push-

back from people saying, “Well, this is all well and good, but it

will only work for motivated students.” And they say it assum­

ing that maybe 20 percent of students fall into that category. I

probably would have agreed with them seven years ago, based

on what I’d seen in my own experience with the traditional aca­

demic model. When I first started making videos, I thought I

was making them only for some subset of students who cared—

like my cousins or younger versions of myself. What was truly

startling was the reception the lessons received from students

whom people had given up on, and who were about to give up

on themselves. It made me realize that if you give students the

opportunity to learn deeply and to see the magic of the universe

around them, almost everyone will be motivated.


it will be interesting to see just how many students care.



Is Khan Academy, along with the intuitions and ideas that

underpin it, our best chance to move toward a better educa­

tional future? That’s not for me to say. Other people of vision

and goodwill have differing approaches, and I fervently hope

that all are given a fair trial in the wider world. But new and

bold approaches do need to be tried. The one thing we cannot

afford to do is to leave things as they are. The cost of inac­

tion is unconscionably high, and it is counted out not in dol­

lars or euros or rupees but in human destinies. Still, as both an

engineer and a stubborn optimist, I believe that where there are

problems, there are also solutions. I f Khan Academy proves to

be even part of the solution to our educational malaise, I will

feel proud and privileged to have made a contribution.


indeed, never trying anything new implies no progress.


reminds me of another book i want to read soon.