mteb/BrightProPsychologyRetrieval huggingface dataset:
21. Some researchers have compared the differences and influencing factors of the mathematics midterm examination results of male and female students in first round of CEPS, and proposed that the high educational expectations of female student’s parents and the high self-discipline of female students can help them overcome the negative effects of gender stereotypes. The examination-oriented education also increases the possibility of female students to achieve good results through hard work (Chen and Bai  2020  ).
#####  Topics
* Matthew B. Ross  1  ,
* Britta M. Glennon   ORCID:  orcid.org/0000-0003-4702-8599  2  ,  3  ,
* Raviv Murciano-Goroff   ORCID:  orcid.org/0000-0003-0361-6225  4  ,
* Enrico G. Berkes   ORCID:  orcid.org/0000-0001-5117-9918  5  ,
* Bruce A. Weinberg   ORCID:  orcid.org/0000-0001-8856-1803  3  ,  5  &
* …
* Julia I. Lane   ORCID:  orcid.org/0000-0002-8139-2960  6
Erin G. Teich, Jason Z. Kim, … Dani S. Bassett
* 1  Department of Psychology.
** About Pew Research Center  ** Pew
Research Center is a nonpartisan fact tank that informs the public about the
issues, attitudes and trends shaping the world. It conducts public opinion
polling, demographic research, media content analysis and other empirical
social science research. Pew Research Center does not take policy positions.
It is a subsidiary of  The Pew Charitable Trusts
 .
* Article
*  Open access 
* Published:  22 June 2022
10 February 2023
The resulting analytical dataset consists of 21,133,102 potential authorship
observations (17,929,271 on articles and 3,203,831 on patents) of which
367,231 were actual authorships. 43.8% of potential authorships were by women,
whereas 31.8% of actual authorships were by women. If these numbers are
converted to rates, the weighted attribution rate on scientific documents was
3.17%. The attribution rate for articles alone is 3.2% while it is 1.3% for
patents (Extended Data Table  2  ).
Although both of these attribution rates are relatively low, this is largely
owing to the inclusion of undergraduate students and research staff in our
sample as well as those observed working for short time periods. These
employees are rarely observed in the actual authorships and result in a lower
the overall attribution rate. The regression analyses reported in the
subsequent sections control for both position and the number of days worked in
the team; part 1 of the  Supplementary Information
 provides results excluding
undergraduates and research staff. The results are robust in each
specification.
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Negative stereotypes can influence individuals of the target social group
through internal and external channels. The external environment (such as
family and school) can cause group members to internalize the stereotype
through socialization, and the stereotype threat constitutes an external
pressure on the individual. The negative self-stereotype held by female
students, that is, the acceptance and internalization of “women are not good
at mathematics,” causes them to lack self-confidence in learning mathematics
and leads to the loss of learning motivation, low self-efficacy in learning,
and ultimately poor performance in mathematics (creating a self-fulfilling
prophecy). Of course, not all female students would agree that “women are not
good at mathematics,” and some female students perform well in math. For this
group of female students, the bias toward women's mathematics learning in
daily life may constitute an external contextual pressure (stereotype threat)
since they can sense this bias and fear that their own mistakes in math
learning would provide others with the evidence that “female students are not
good at learning mathematics.” Thus, they may feel anxious, nervous, stressed
out, and overcautious in learning and testing, which affects their
performance, especially when they are dealing with difficult mathematical
problems (Spencer et al.  1999 : 4–28.") ). As female students tend to attribute their failure to
capability, they may lose their interest, motivation, and perseverance in
learning mathematics and become less confident in learning mathematics when
they enter a higher level of school; furthermore, their subsequent learning
results and major selection may be affected (Mok et al.  2011
: 87–104.") ;
Correll  2001 : 1691–1730.") ).
* Science*
###  Hypothesis 1a
The construction of research teams was informed by the work of Stephan   30
.") , who operationalized the concept of
a research team to be a collection of scientists working jointly on projects
with common funding and resources. The UMETRICS data are ideally suited to
create measures of teams at scale using this definition, because the
administrative data provide detailed information of all people charging time
to each grant in each payroll period   31 .") , 
32 .") .
* Received  :  25 February 2021
Julia I. Lane
short reads  |  Sep 18, 2023
A number of studies on the gender views of mathematics teachers abroad have
found that teachers tend to agree that mathematics is a male field. When
evaluating male and female students with similar mathematics performance,
teachers may have a higher evaluation of male students’ mathematical
capability. They are more inclined to attribute male students’ excellent
mathematics performance to their inherent capability and that of female
students to their efforts (Li  1999
: 63–76.") ). This clear-cut gender division is more common and
straightforward in the research published in China. When explaining the
difference between males' and females' mathematics scores, the following types
of explanations are common: “in the aspect of thinking pattern, women tend to
imitate, men tend to think independently; women are attentive, men are
flexible … women are quiet, docile, and not good at communication; men are
active and relatively independent; women pay attention to classroom teaching,
and men pay attention to extracurricular knowledge” (Zhang and Zhang  2003
: 59–61.") :60). Some have
even proposed that “the college entrance examination should take the
characteristics and differences in the thinking patterns between men and women
into consideration and pay attention to the issue of gender inclination…
reducing the usage of technical language in favor of daily language will
impose higher requirements for language expression and writing, and will
become a great advantage for women who are good at using the left brain” (Ye 
2011  :42).
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Numbers, Facts and Trends Shaping Your World
_Internal and Emergency Medicine_ (2024)
On average,
women working in STEM jobs are more likely than men to say they have
experienced workplace discrimination due to their gender. Half (50%) of women
in STEM jobs say they have experienced any of eight forms of discrimination in
the workplace because of their gender – more than women in non-STEM jobs (41%)
and far more than men in STEM occupations (19%). The most common forms of
gender discrimination experienced by women in STEM jobs include earning less
than a man doing the same job (29%), having someone treat them as if they were
not competent (29%), experiencing repeated, small slights in their workplace
(20%) and receiving less support from senior leaders than a man who was doing
the same job (18%).
###  Qualitative evidence
Table  2  shows the estimation
results of multilevel linear regression.  Footnote  16  The series of Model 1
is for male students, and the series of Model 2 is for female students to
investigate the impact of various relevant factors on boys or girls and to
enable a comparison. Each series has three models (A, B, and C). Model B and
Model C include the interactive variables of gender stereotypes of
students/parents and the length of junior high school.  Footnote  17
9. The CEPS cognitive capability test includes language capability and mathematical ability. For specific information about the test, please refer to  http://ceps.ruc.edu.cn/indeX.php?r=index/technologyReport  .
###  Independent variables
All Publications   Methods
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* 6031  Accesses
41. Survey of earned doctorates. _National Science Foundation_  https://www.nsf.gov/statistics/srvydoctorates/#qs  (2022).
*  EXAMINING THE GENDER GAP IN ENGINEERING PROFESSIONAL IDENTIFICATION.
2. Xinyu Liu
Correspondence to  Julia I. Lane  .
The evidence generated from the analysis described in this paper suggests that
Rosalind Franklin is far from unique in not receiving credit for her work. If
credit is defined simply as ever being named an author, women account for only
34.85% of the authors on a team, even though they make up just under half of
the workforce (48.25%; Extended Data Table  2
 ). When credit is defined as the
likelihood of being listed as an author on a given document (relative to the
mean) produced by a research team, there is a 13.24% gap for articles and a
58.40% gap for patents in the likelihood that women are named on any given
article or patent produced by their team (Extended Data Table  4
 , column 5). The chances of women
receiving credit on an article decrease by 4.78% relative to the baseline rate
of 3.18% ( _P_ < 0.0001; two-sided _t_ -test; test value = −3.8, effect size =
−0.0015 percentage points (pp)) for each 1 log point increase in citations
(Extended Data Table  7  ).
Business & Workplace   Black Americans
  Asian Americans   Medicine & Health
 
Education & Gender   Discrimination & Prejudice
  STEM Education & Workforce
 
Higher Education   Education
* Mathematics*
###   Celebrating Women in STEM
_Nature Human Behaviour_ (2023)
###  Hypothesis 4b
Research on the relationship between gender structure and the number of
children and mathematics learning has not achieved consistent results. Some
studies have found no significant difference in mathematics scores between
urban single-child daughters and sons (Tsui and Rich  2002
: 74–92.") ), and other studies have found that the
math scores of single-child students are significantly higher than those of
students having siblings (Zhang and Xie  2015
:
19–34.") ). Some studies have found no significant difference in mathematics
scores between rural single-child students and students with siblings (Nie et
al.  2016 : 5–16.") ). However, there are few studies on
whether the influence of gender structure on mathematics learning is similar
for female and male students.
Mathematical capability is the foundation of STEM disciplines and plays the
role of a “filter” (Correll  2001 :
1691–1730.") ). Mathematical knowledge is the indispensable basis for
continuing STEM learning and STEM-related occupations.  Footnote  2  The
performance of a middle school student in mathematics is directly related to
whether she or he will choose STEM-related majors and employment in the future
(Ing  2014 : 87–103.") ).
Therefore, this study uses mathematics learning in junior high school as the
basis to explore the social and cultural factors hindering Chinese females’
progress in STEM education. It focuses on females' perception of gender
stereotypes, that is, whether they believe males are better at mathematics
than females at the student, family, school, and social levels. The data of
this study are derived from the China Educational Panel Survey (CEPS)
conducted by the National Survey Research Center (NSRC) at Renmin University
of China.  Footnote  3
For each pay period, the FHR system at each university records the details of
charges to each sponsored project, including for each person paid on each
grant and reports the information to the Institute for Research on Innovation
and Science   37 .") . These administrative data are different
from the level-of-effort data that are submitted by PIs as part of their
annual and final report to an agency in at least three ways. First, they
represent actual payroll data, drawn from the FHR system every pay period,
rather than the estimate provided by the PI or the team administrator once a
year. An intensive hand-curated effort that compared the results from an early
effort found that the FHR reports are more granular and comprehensive than the
PI or team administrator reports   38 .") ,  39 .") . For example, all personnel names (including co-PIs) are recorded
in the FHR reports, but many names are not recorded in the former. Second, the
UMETRICS data capture all sources of funding, and are much more comprehensive
than data from a single agency. The UMETRICS data include federal funding
sources as well as funding from philanthropic foundations, state and local
governments, industry, and international organizations. Third, the data
reflect actual expenditures in every accounting time period, not just funds
that are obligated at the beginning of a grant. So if, as often happens, there
is a no-cost extension, or more funds are spent earlier in the project, that
spending and the work of the relevant team members is captured in the data.
There are limitations. If personnel do not charge time to the grant, their
effort is not captured in the data; we are unaware of any source that would
capture unpaid work. If there are gender differences in unpaid research work,
the analysis would not be able to capture such differences.
##  Ethical approval
###   The science of inequality
4. PISA was initiated by the Organization for Economic Cooperation and Development (OECD). The survey subjects are students aged 15 years old. Since 2000, the three fields of reading, mathematics, and science have been evaluated every three years (see details and previous reports in  http://oecd.org/pisa/  ).
###  Hypothesis 4a
The probability that an individual in a team is an author on an article (left)
or inventor on a patent (right) in relation to the number of citations that
the document receives. Estimates were obtained from an ordinary least squares
regression of the probability of being named with an indicator for gender
against the log of total forward citations plus one (Extended Data Table  7
 ). Left, the regression is estimated
based on 17,929,271 potential article authorships. Right, the regression is
estimated based on 3,203,831 potential patent inventorships. The observations
are weighted by the inverse of the number of teams per employee times the
inverse of the number of potential articles or patents per employee. Estimates
include controls for publication date (calendar year × month), PI status,
number of days worked on the team, job title and research team fixed effects.
Each data point represents the estimated difference in the probability of a
woman being named an author (left) or inventor (right) at each citation level.
Error bars extend from the point estimate of the estimated marginal effect by
±1.96 × the standard error and show the 95% confidence interval of the
marginal effect. Standard errors are clustered by team and employee.
One respondent explained it this way:
Furthermore, some foreign researchers have pointed out that with the
improvement of females' situation in mathematics learning, the focus on the
gender gap in mathematics performance has shifted from the average to the two
ends of the performance distribution (low score and high score groups),
especially the highest performing groups; that is, although there is no
significant difference between males and females in the average performance
(before secondary education) in math, the fact that there are more males and
fewer females in the group with the best mathematics performance has not
changed (Halpern et al.  2007 : 1–51.") ). However, we
believe it is still valuable to pay attention to the difference between the
average scores of males and females in mathematics in China, which has not
been sufficiently addressed. We are not very clear about the exact change
trend and the underlying mechanism driving the difference between males and
females in mathematics learning from elementary to senior high school, nor are
we very clear about the process and influencing factors by which females are
screened out the STEM fields. Therefore, while the concern of foreign
researchers has gradually started to change from “why women cannot” to “why
women are not willing to” enter STEM fields for study or employment, we still
need to make efforts to solve the problem of “why they cannot.”
5. There was no statistical significance in gender difference among the students in Shanghai who participated in the PISA assessment in 2009, the students in Tianjin who participated in the pilot assessment in 2012, and the students in the four provinces and cities who participated in the assessment in 2015. However, in the 2018 test, the average performance of males in mainland China was significantly higher than that of females. For relevant data, please refer to Guan (  2017 . Journal of Mathematics \(china\) 56 \(9\): 47–51.") ), Zhong (  2014  4: 64–67.") ), Table I.5.8a in the PISA 2015 report, and Table II.B1.7.3 in the PISA 2018 report.
The psychological test on stereotype threat confirmed the existence of
stereotype threat from two perspectives: gender combination (with male
participation or not) and psychological suggestion (Spencer et al.  1999
: 4–28.") ; Niederle and
Lise  2010 : 129–144.") ). Other
empirical studies have found that the traditional gender view of the class
will affect students' academic achievements (Salikutluk and Heyne  2017
: 368–381.") ). These studies inspired us to
measure the threat of gender stereotypes through the gender attitudes of
opposite-gender classmates. However, some schools rearranged the classes after
the first round of the survey, and thus, information on some classes was not
complete in the follow-up rounds. In addition, CEPS only inquired into the
gender stereotypes of the students during the first round of the survey.
Therefore, based on the class information in the second round of the survey,
the average gender stereotype held by opposite-gender classmates in grade 7
was used to measure the stereotype threat faced by students.
##  Data and methods
Politics & Policy  
International Affairs   Immigration & Migration
  Race & Ethnicity
  Religion
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  Gender & LGBTQ
* ###   Collective action is needed to build a more just science system
Xinyu Liu
#####  Topics __
What the data says about food stamps in the U.S.
####  What’s a STEM job?
#####  Regions & Countries
3. Beijing, China
The scientific field of each team is identified by using the title of all
associated grants and comparing the grants with a pool of text that describes
each scientific field using a wiki-labelling approach  17  ,  18  ,  19
.") . This
approach is used to assign a likelihood score that a given grant award title
belongs to a given research field category, as categorized by the NCSES Survey
of Graduate Students and Postdoctorates in Science and Engineering. Each
team’s field is estimated by taking the field of each grant and weighting the
grant’s relative importance to the team’s portfolio by the direct expenditure
of each grant over the analysis period.
Politics & Policy  
International Affairs   Immigration & Migration
  Race & Ethnicity
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  Family & Relationships
  Economy & Work
  Science
  Internet & Technology
  News Habits &
Media   Methodological
Research   Full
topic list
*  The perceived role fit of women and men academics: evidence from the social sports sciences.
* Technology*
###   Discrimination Experiences Shape Most Asian Americans’ Lives
_Nature Geoscience_ (2023)
*  Vocational interests in the United States: Sex, age, ethnicity, and year effects.
##  Attribution and administrative data
Majority in U.S. Say Israel Has Valid Reasons for Fighting; Fewer Say the
Same About Hamas
Gender differences in observed scientific output are well-documented: women
both publish and patent less than men   1 .") . The causes of these differences are not well
understood. Analysis using individual data has suggested that women are less
productive because they work in less welcoming work environments   2
.") , have greater family responsibilities   3
.") , have different positions in the
laboratory   4 .") or differ in the type of
supervision they are provided   5 .") . Recent work has suggested that women are not
less productive, but rather that their work is undervalued   8
.") . The analysis in this Article uses new data on research
teams to suggest that women are accorded less credit than men: they are
systematically less likely to be named as authors on articles and patents.
4. Enrico G. Berkes
Women are credited less in science than men
Finding ‘what isn’t there’ from ‘what is there’ is a fundamental problem in
statistics, and has been used to address such vastly different questions as
calculating the return on investment of mutual funds (after accounting for
funds that no longer exist) or the optimal placement of armour on aeroplanes
in the Second World War   12 .") (after accounting for those that did not return). The
problem of selecting on the dependent variable is also prevalent in the social
sciences; for example, in only observing the labour supply of people who
participate in the labour market   13 .") or studying the
drivers of economic development by selecting a few successful industrializing
countries   14 .") .
Affiliations  Expand
###  Dependent variables
Based on the influence of same-gender peer groups on female mathematics
learning discovered by researchers (Raabe et al.  2019
:
105–123.") ; Riegle-Crumb et al.  2006
: 206–228.") ), we put forward
the following assumption.
###   Striking findings from 2023
*  Women's underrepresentation in science: sociocultural and biological considerations.
Graphs show the probability that an individual in a team is an author on a
given article (left) or patent (right) published by that team. Left, the
likelihood of attribution on an article is estimated from 17,929,271 potential
authorship observations. Right, the likelihood of attribution on a patent is
estimated from 3,203,831 inventorship observations. The data associated with
each bar are generated by predicting the dependent variable from ordinary
least squares regressions of the likelihood of being named on gender and the
indicated controls (reported in Extended Data Table  4
 ). For the purpose of plotting
probabilities and gender differences holding all else fixed (Δ), we hold all
of the controls at their respective means. Because men have higher values than
women on average on the controlled factors that increase the probability of
attribution, the predicted probabilities for men decline and those for women
increase as more controls are included. Controls, from left to right: (1)
none; (2) whether a potential author is the PI of the team, the number of days
worked on the team and publication date (calendar year × month); (3) job title
of the potential author/inventor; (4) research field of the team; (5)
individual indicator variables for each team (these team indicators subsume
the fields indicator). The observations are weighted by the inverse of the
number of teams per employee times the inverse of the number of potential
articles or patents per employee. Individual data on the probability of women
or men being named on articles or patents are visualized in Supplementary Fig.
 6  . Error bars are centred on the
mean and extend to the 95% confidence interval based on 1.96 × s.e. Standard
errors are clustered by team and employee.
The idea that “women are not good at math” or “men are better at math than
women” is shared worldwide. The “Implicit Project” of Harvard University
released a set of implicit association tests online. From May 2000 to July
2008, more than 500,000 people around the world participated in the tests, of
whom more than 70% linked science/mathematics with men, while women were more
often connected with literary subjects (Nosek et al.  2009
: 10593–10597.") ).