mteb/BrightProPsychologyRetrieval huggingface dataset:
A similar pattern is apparent when authorship is analysed by field (Fig.  1
 , right). For example, in biology, the
share of actual authorships who are women is 15.02 pp lower than the share of
women among potential authors ( _P_ = 0.0000; two-sided _t_ -test; test value
= −3,024; effect size: 15.02 pp; Extended Data Table  3
 ). In physical science, the corresponding
difference is 14.12 pp ( _P_ = 0.0000; two-sided _t_ -test; test value =
−25.44; effect size = 14.12 pp; Extended Data Table  3
 ). Note that the figure does not control
for job title in disaggregating attribution by field, so fields with
disproportionately more women and lower attribution rates may reflect the fact
that there are more research staff.
* Comparative Study
###  Related
Figure  1  (and Supplementary Fig.  5
 ) makes use of information in the data
about each individual’s position in the organization—faculty, postdoc,
graduate student, undergraduate student or research staff—as well as the
research team’s field. Women occupy more junior career positions than men. The
proportion of women in each position declines as the seniority of the position
increases (Fig.  1  , left). At the high
extreme, 34.82% of faculty members are women; at the lower extreme, 60.81% of
research staff are women.
###  Model specification
Asians working in STEM jobs are based on those who self-identify as Asian or
Asian American and work in occupations classified as STEM. There are too few
Asians working in non-STEM jobs in the survey for separate analysis. Note that
the survey was conducted in English and Spanish only; thus only Asians
proficient in English/Spanish are likely to have completed the survey. For
more on the characteristics of the Asian population in the U.S. see the
Center’s  fact sheets 
on Asian Americans.
#  Women are credited less in science than men
short reads  |  Dec 8, 2023
A stereotype is a cognitive structure composed of knowledge, concepts, and
expectations of certain social groups (such as gender, ethnicity, and
religion). It is the product of cognitive, emotional, socio-motivational, and
cultural factors and processes or their interaction (Mackie et al.  1996
 :42). Stereotypes can
be positive or negative and can be self-stereotypes (stereotypes toward self)
or other-stereotypes (stereotypes toward other people). The gender stereotype
in mathematics learning, that is, “boys have better math performance than
girls,” is negative for female students but positive for male students.
####  Analytical sample
Concerns about
the underrepresentation of blacks and other racial minorities – and
particularly women of color – in the STEM workforce have been ongoing for at
least four decades.  8  The Pew Research Center survey finds that, today,
black STEM workers are especially likely to say they have experienced
discrimination at work because of their race or ethnicity; 62% of blacks in
STEM say this, compared with 44% of Asians, 42% of Hispanics and just 13% of
whites in STEM jobs.
Other  Pew Research Center analyses  found that black Americans with at least some college
experience are more likely to say they have experienced discrimination or been
treated unfairly across a range of experiences because of their race or
ethnicity, compared with those without any college experience. (There are not
enough blacks in STEM jobs in this survey for analysis by levels of
education.)
Gender equality was initially achieved in educational attainment at the
national level, but there are still gaps in more profound and detailed
aspects, such as the content and process of education. For example, there is
significant gender segregation in the distribution of disciplines—the
disadvantages of female students in science and engineering disciplines
(including study and employment) have not been fundamentally reversed. In high
school, the majority of female students who performed well in mathematics in
elementary school nevertheless chose to study liberal arts, which determines
that their college majors and career development can no longer be linked to
the scientific and technological fields. In 2019, the proportion of women in
scientific and technological research and development was only 26.0% at the
national level (Department of Social Science, Technology, and Cultural
Industry in the National Bureau of Statistics, Department of Innovation and
Development in the Ministry of Science and Technology  2020
 ).
Psychol Bull
Qualitative evidence about the reasons behind the lack of attribution can be
obtained from surveys. Despite the well-known issues with selection bias,
self-reporting and low response rates, survey data can be useful for
triangulating against administrative data   20
.")
. We designed a survey of authors who appeared on at least one article in the
Web of Science   21 .") after 2014 and who had a published and available e-mail address.
We asked three core sets of questions of each individual to shed light on the
findings from our analysis of administrative data (the full survey is
reproduced in  Supplementary Information
 , part 3).
Rong Su  1  ,   James Rounds
 1  ,   Patrick Ian Armstrong
 1
###  Empirical strategy
At the PSU/county level, existing international comparative studies have found
that females' mathematics performance is related to the degree of economic
development (Guiso et al.  2008  ). In addition, the more equal the
gender status of a country is, the more favorable it is to improving the
mathematics performance of females and reducing the gender difference (Penner
 2008 : S138–S170.") ). However, the tradition of son preference and
skewed sex ratio at birth persists in Chinese society, although the
traditional fertility mode has changed to some extent. Given this factor, we
constructed three local-level control variables based on the national census
data in 2010: The first is the gender ratio of the age group (10–14 years old)
of the interviewed students (the gender ratio of the same group), which was
used to measure the degree of son preference or gender inequality in the local
society.  Footnote  11  The second is the creation of an education gender
equality index (the difference between the average education years of males
and females divided by the average years of education in the local population
aged six and above). The larger the value is, the greater the extent to which
males lead females in education. Third, the proportion of the local people
aged 15 and above who have received a college education (undergraduate
education) is used to indirectly measure the local education development and
the local social and economic development.
###  Survey design and collection
* ###   Quantitative research assessment: using metrics against gamed metrics
Among the control variables, we found that most of the control variables had
the same effect on boys and girls.  Footnote  18  For both male and female
students, participating in a mathematics tutorial can significantly improve
their performance on the mathematical capability test. Parents’ education
level and the average socioeconomic status of school can also significantly
improve the mathematical capability of students. The higher the education
level of parents or the average socioeconomic status of the school is, the
better the mathematical capability of the students is. These findings are
consistent with other researchers' findings (Halpern et al.  2007
: 1–51.") ). In addition, when controlling for the
socioeconomic status of schools, there is no significant relationship between
the ranking of schools in the local enrollment rate and the mathematical
capability of students; that is, the mathematical capability of students
studying in schools with a relatively high enrollment rate is not
significantly better than that of students studying in schools with a slightly
lower enrollment rate.
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Patents are drawn from the PatentsView visualization and analysis platform,
which contains 6.8 million total patents dating from 1976 to 2018   43
.") . The
analysis focuses on a subset of patents that have application dates between
2014 and 2016 and are linked to individuals observed in UMETRICS from
2013–2016. Additional robustness checks on other year ranges are include in
the  Supplementary Information  .
It is possible, of course, that the gender differences arise from
compositional differences between women and men in the teams on which they
work, fields, job titles or time allocated to particular projects. In
particular, women might sort into teams with different propensities to publish
or onto projects with different research questions. Figure 2 (and SI Figure
S6) plots the estimated attribution rate for men and women on articles (left)
and patents (right) as well as the differences (indicated by Δ). Using a
series of regression models that control for these types of potential
compositional differences, we estimated the attribution rate for men and women
on articles and patents as well as the differences (indicated by Δ) (Fig.  2
 , Extended Data Table  4
 and Supplementary Fig.  6
 ). In these models, an indicator for
being named is regressed on an indicator for gender as well as an increasingly
expansive set of control variables (Extended Data Table  4
 ). Column (1) includes no controls;
column (2) adds publication date (calendar year × month), days worked on the
team, and an indicator for the individual being a PI; column (3) adds job
title indicators; column (4) adds field controls; and column (5) adds
indicator variables for each team. Including these additional controls
reduces, but does not eliminate, the disparity for women. Even in the fully
specified model, which adds controls for each research team, women are 13.24%
( _P_ < 0.0001; two-sided _t_ -test; test value = −6.3788; effect size =
−0.4210 pp) less likely to be named on articles and 58.40% ( _P_ <0.0001; two-
sided _t_ -test; test value = −10.7746; effect size = −0.7652 pp) less likely
to be named on patents.
The same question—whether women with the same contribution as men are less
likely to be credited—can be asked a different way: conditional on being
credited, did women contribute more than men? Accordingly, we asked authors to
indicate what they did to earn authorship on one of their most recent
publications using the standardized contributions identified by Project Credit
 22 .") . The results,
reported in Fig.  5  and in Supplementary
Fig.  7  , are consistent: on average,
women have to do more than men to be included as an author (2,297 individuals
responded: 1,371 men and 926 women). A simple unweighted count of total
contributions reported shows that women report a total 6.34 contributions on
average compared with 6.11 for men ( _P_ = 0.0907; two-sided _t_ -test; test
value = −1.6925; effect size = −0.2376). Women report making significantly
more contributions in conceptualization (64.99% of men versus 68.36% of women;
_P_ = 0.0937; two-sided _t_ -test; test value = −1.6767; effect size =
−0.0337), data curation (37.42% of men vs. 44.38% of women; p = 0.0008; two-
sided _t_ -test; test value = −3.3467; effect size of −.0697), writing the
original draft (45.73% of men versus 52.48% of women; p = 0.0015; two-sided
_t_ -test; test value = −3.1813; effect size = −0.0675) and reviewing and
editing (82.57% of men versus 86.18% of women; p = 0.0205; two-sided _t_
-test; test value = −2.3178; effect size = −0.0361). The only category in
which men reported a greater contribution was software (18.31% of men versus
11.67% of women; p = 0.0000; two-sided _t_ -test; test value = 4.3174; effect
size = 0.0664). There is no significant difference between men and women in
either formal analysis (49.23% of men versus 51.94% of women; _P_ = 0.2028;
two-sided _t_ -test; test value = −1.2740; effect size = −0.0271) or project
administration (32.82% of men versus 35.75% of women; _P_ = 0.1471; two-sided
_t_ -test; test value = −1.4504; effect size = −0.0292).
short reads  |  Sep 12, 2023
Second, from the perspective of class, most students participating in the
survey were currently or had been in a class with obvious gender stereotypes.
Among the 221 classes participating in the second round of the survey, 125
classes (56.6% of the classes and 55.4% of the students) had an average
stereotype score of more than 0.5 (not shown in Table  1
 ). When students in the class are
divided into two gender groups, the class environment of males and females is
not different. The average stereotype score of male students in the class is
approximately 0.60, and that of female students is approximately 0.44.
Given the above controversies, this study uses mathematical capability as the
dependent variable, which is a time-varying variable; that is, the value of
the variables varies with the change in grade (hereinafter referred to as
“student/grade”). During each round of the CEPS survey, two sets of variables
that can be used to measure mathematical capability are collected. One set is
the results of midterm examinations organized by schools. This kind of
examination may be considered an examination of book knowledge instead of
measuring students' mathematical capability (Halpern et al.  2007
: 1–51.") ). Moreover, it is difficult to compare
across schools. Hence, we have adopted the second set of variables, that is,
the results of the mathematical-related cognitive capability test implemented
in each round of the survey, which includes two parts: “graphics and space”
and “calculation and logic.” The purpose is to “measure students’ basic
logical thinking and problem-solving capability…The content does not involve
the specific knowledge taught in the school curriculum.”  Footnote  9  After
obtaining the original scores of the test, the project team converted them
into 3PL model standard scores based on item response theory (IRT) to compare
different schools, regions, and rounds.
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_The Journal of Chinese Sociology_  ** volume  10 ** , Article number:
10  (  2023  )  Cite this article
8. The survey was carried out in 2013–2014 academic year. The second and third rounds of follow-up visits were conducted in 2014–2015 and 2015–2016 academic year. For details of sampling design, questionnaire\survey volume\project implementation, please refer to  http://ceps.ruc.edu.cn  .
*  Normative changes in interests from adolescence to adulthood: A meta-analysis of longitudinal studies.
Ji-Young Son & Michelle L. Bell
report  |  Nov 30, 2023
Third, consistent with the findings of some studies on foreign teachers (Li 
1999 : 63–76.") ), the teachers participating in CEPS also showed
obvious gender stereotypes: 44.9% of the teachers completely agreed or
somewhat agreed that “there is an inborn difference between males and females
in mathematics or English learning” (the value is 3), 36.8% of the teachers
completely disagreed or relatively disagreed (the value is 1), and 18.4% of
the teachers chose “neutral” (the value is 2). The median score of teachers'
attitudes in each school is 2, and the average is 2.06 points. Therefore, in
general, teachers are slightly inclined to agree with the existence of gender
differences related to stereotypes.  Footnote  14
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Peer groups and teachers constitute significant others that affect the
attitudes and behaviors of adolescents. Studies on secondary schools at home
and abroad have confirmed that the peer effect has a significant effect on
educational attainment in secondary school (Palardy  2013
: 714–754.") ; Ding and Lehrer  2007
: 300–312.") ), but the degree of
validity may vary by gender. Some studies have found that boys are more
vulnerable to the influence of peer group culture; therefore, a learning-
oriented peer culture is more conducive to boys' learning, while a peer
environment that does not respect learning may make boys more resistant to
learning, whereas females are less influenced by peer culture (Legewie and
DiPrete  2012 : 463–485.") ). However, the
above assertions have not been fully verified in research on mathematics
learning. Studies on middle schools in the United States and Sweden have found
that the attitude or achievement of same-gender peers or same-gender
classmates toward mathematics/STEM has a positive impact on female performance
in mathematics but has no effect on male performance in mathematics (Raabe et
al.  2019 :
105–123.") ; Riegle-Crumb et al.  2006
: 206–228.") ). The gender-based
peer effect may exist because female peers who perform well in
mathematics/STEM courses act as role models for girls, helping them resist or
eliminate the negative effects that stereotypes may bring about, while the
stereotype of mathematics is beneficial to boys, so boys do not need the
promotion effect of peer culture (Riegle-Crumb et al.  2006
: 206–228.") ). However, a study
of secondary schools in four European countries found that the mathematics
performance of both male and female students was affected by the gender
attitude of the class, and the mathematics performance of male students in the
class that was more inclined to the traditional gender norms was higher, while
that of female students was lower (Salikutluk and Heyne  2017
: 368–381.") ).
* John P. A. Ioannidis
* Zacharias Maniadis
####  Scientific fields
5 facts about religion and Americans’ views of Donald Trump
From voter registration to mail-in ballots, how do countries around the
world run their elections?
Many explanatory studies either support or oppose the theory of physical
differences (brain structure, androgens, and inborn preference etc.). In
recent years, however, more studies have questioned the explanatory power of
physical factors. Moreover, neither an inborn physical basis nor individual
preference can produce actual competence without education, training, and
learning, and whether there is a fundamental difference in physical structure
related to mathematical capability between men and women is debatable. In
addition, individual cognitive capability, including spatial and mathematical
capability, can be improved through acquired learning and practice, both for
males and females. In the long process of learning and training, it is
difficult to distinguish the influence of inborn factors from acquired
factors. Therefore, physical factors cannot be the only or decisive mechanism
to explain the gender difference in mathematical capability (Halpern et al. 
2007 : 1–51.") ; Ceci et al.  2014
:
75–14.") ).
Son preference in the family may affect children’s educational attainment,
especially that of girls. A study of Taiwanese families found that with
limited family economic resources, parents are likely to sacrifice educational
opportunities for girls, especially older girls. However, in recent years, the
distribution of family educational resources has become increasingly
independent of the number and gender of children, and boys and girls tend to
be equal in educational opportunities (Parish and Willis  1993
: 863–898.") ; Chu et al.  2007
: 91–113.") ).
This finding has also been partially verified in family studies in the Chinese
mainland. The implementation of the family planning policy and rapid economic
development have greatly promoted gender equality in education. Many studies
on urban single-child families have found that even if parents with only one
daughter believe that the capability of women is not as high as that of men,
there is no significant difference between their education expectations and
investment and that of parents with one son (Tsui and Rich  2002
: 74–92.") ; Fong  2002
: 1098–1109.") ). An analysis of families with
multiple children also found that gender is no longer an important factor
affecting access to education (Zhang and Xie  2015
:
19–34.") ). However, an analysis of rural families also shows that although
the reduction in the number of children has promoted the educational
attainment of girls and narrowed the educational gap between boys and girls
(Guo and Liu  2013 :
34–39.") ), the son preference of parents and the concept of “raising sons for
old age caring” still have a certain impact on the distribution of educational
resources. The existence of sisters will not affect the educational attainment
of boys, but the existence of brothers is not conducive to the educational
attainment of girls (Zhang et al.  2013
 ).
A series of regressions was estimated. The first set (Extended Data Table  3
 ) included controls,  \\({X}_{i,e}\\)  ,
which sequentially include indicator variables for the publication or
application month associated with a potential authorship or inventorship, the
team’s PI, the number of days worked in the team, and an indicator of whether
the individual’s gender was unknown. Idiosyncratic trends in the data are
accounted for by including a series of  \\({M}_{i,t}\\)  calendar year ×
months and year fixed effects based on the date when article _i_ was published
or patent _i_ applied for; an individual’s position in the team is accounted
for through a series of  \\({O}_{i,e}\\)  position variables that capture the
days that an individual worked in a particular position as a share of the
total days worked on the research team. Differences across research teams are
accounted by including a series of  \\({{\rm{Team}}}_{i,l}\\)  team fixed
effects and we denote the disturbances in the data using  \\({\mu
}_{i,t,e,l}\\)  . The second set (Extended Data Table  5
 ) re-estimated equation (  1
 ) with the same controls but by job
title; the third set (Extended Data Table  6
 ) re-estimated the same equation with the
same controls by field. The final set (Extended Data Table  7
 ) examined high-impact publications and
patents.
Qualitative analysis—open-ended narrative statements by survey respondents as
well as personal interviews with consenting authors (approach detailed in
Methods, ‘Survey design and collection’ and ‘Qualitative evidence’ and
Supplementary Information, part  3 
)—was also consistent. Authors noted that the rules of credit allocation were
frequently unclear and often determined by senior investigators. A complex mix
of factors, particularly field, rank, culture and gender, was identified.
However, an overarching theme was that the rules governing scientific
contributions were often not codified, not understood by all members of the
research team, or simply ignored. The necessary level of work required for
authorship is often not clear to everyone participating on research teams, and
the level of work deemed necessary to receive attribution can vary on the
basis of the idiosyncratic personal preferences and a team member’s
relationship with the principal investigator (PI). Thus, women and other
historically marginalized groups must often put in significantly more effort
in order for their scientific contributions to be recognized.
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