mteb/BrightProPsychologyRetrieval huggingface dataset:
The survey finds
a higher share of blacks in STEM jobs report experiencing any of eight types
of racial/ethnic discrimination (62%) than do others in STEM positions (44% of
Asians, 42% of Hispanics and just 13% of whites in STEM jobs say this). They
also tend to do so more than blacks in non-STEM jobs (50%), with many saying
they have been treated as if they were not competent because of their race or
ethnicity.  2  Blacks in STEM jobs are particularly likely to say there is too
little attention to racial and ethnic diversity where they work (57%). And,
when it comes to the way opportunities for advancement and promotion are
handled in their own workplace, 37% of blacks in STEM jobs believe that blacks
are usually treated fairly, while a similar share (36%) says this sometimes
occurs and 24% believe that blacks are usually treated unfairly where they
work. Among Hispanics, those in STEM and non-STEM jobs are equally likely to
say they have experienced racial/ethnic workplace discrimination.
Table  1  shows the descriptive
statistics of each variable  Footnote  12  divided by gender. In the final
analysis sample, the proportion of male students (52.4%) was higher than that
in the first round of sampling (51.4%).
Much more can be done to unpack the findings in other dimensions, such as the
mechanisms whereby credit for scientific work is allocated, other dimensions
of identity, and richer (for example, non-binary and fluid) measures of
gender. Although we made every effort to be aware of and to guard against
confirmation bias   25 .") ,  26
.") by including a variety of robustness checks in the quantitative
analysis, by working with survey methodologists to review the survey to ensure
that the questions were not leading to a ‘desired’ answer   27
.") , and by developing an interview protocol that did not introduce
any discussion of gender (Supplementary Information, part  3
 ), we encourage other researchers to
work with the code and data that are available at IRIS to extend our analyses.
Indeed, the unique data infrastructure highlighted in this work can be, and is
being, expanded   16 .") by the addition of new universities and links to
many different data sources. It can be used by many other researchers to allow
more examination of the organization of science—ranging from rich and complex
data on the dynamic longitudinal interactions on what is funded (grants), who
is funded (PIs), and the characteristics of the individuals and research teams
who are employed by those funds. It will also be possible in future work to
examine the effect of policies instituted by the research institutions at
which researchers work (at the department, campus and university level   28
.") ) on the retention and productivity of scientists   29
.") , student placements and career trajectories  30  ,  31  ,  32
.") , as well as business startups   33
 229–254 \(NBER, 2019\).") .
6. In mathematics, men may encounter positive stereotypes (men are better at math than women). However, positive stereotypes may also have different effects. If this stereotype is very straightforward (“because you are a boy, you cannot be poor at math”), it may also constitute a stereotype threat and have a stifling effect. However, if the positive stereotype is subtle and does not directly constitute a pressure on the group members, it can promote the performance of the members (Smith and Johnson  2006 : 51–63.") ).
Second level: student
https://www.nsf.gov/statistics/srvydoctorates/#qs
* Bess G. Koffman
* Matthew B. Osman
* Sofia Guest
\(2022\).") . The Survey of Earned Doctorates, an annual survey (with a 93% response rate) of all doctorate graduates from US universities, directly asks respondent to report their gender. The precision of the algorithm was 97.29% for men and 94.06% for women. Robustness checks are reported in the  Supplementary Information  . We note the limitation that our gender construct does not allow for non-binary or fluid gender identities. Addressing non-binary and/or fluid gender identities is an important direction for future research.
First level: grade
doi: 10.1037/a0017364.
Matthew B. Ross
The share of women working in such jobs varies widely both within and across
job types (or clusters). Women account for a majority of healthcare
practitioners and technicians but are underrepresented in other jobs,
particularly computer and engineering positions. While there has been
significant progress for women in the life and physical sciences since 1990,
the share of women has been roughly stable in other STEM occupational clusters
and has gone down 7 percentage points in computer occupations.  11
A survey was sent to 28,000 scientists who had published in an academic
journal listed in the Web of Science and who listed themselves with a public
profile on the ORCID database. The bar chart shows the percentage of 871 men
and women who provided answers to the survey question (Q2b): ‘What is the most
likely reason that you were not listed as an author on that paper?’.
Respondents were able to select more than one option, thus the total number of
responses is higher than the number of respondents. The probability is
computed as the arithmetic mean of the binary responses. Individual data on
the reason an individual is not named are visualized in Supplementary Fig.  7
 . Error bars are centred on that mean
and extend to the 95% confidence interval based on 1.96 × s.e.m. The
difference in the probability of selecting ‘Contribution did not justify
authorship’ between men and women is 0.1294 ( _P_ = 0.0000; two-sided _t_
-test; test value = 4.1060). The difference in the probability of selecting
‘Others underestimated my contributions’ between men and women is −0.0984 (
_P_ = 0.0036; two-sided _t_ -test; test value = −2.9218). The difference in
the probability of selecting ‘Discrimination/stereotyping/bias’ between men
and women is −0.0780 ( _P_ = 0.0003; two-sided _t_ -test; test value =
−3.6623). Additional _t_ -tests of the differences in the probability of
indicating a reason across men and women can be found in the text.
The first steps in identifying the missing data in these two examples are to
describe the population from which the sample of observations is drawn and
then to document the degree of missingness. Subsequent steps then characterize
the sources of the missingness. The large-scale bibliometrics databases used
to study scientific output consist only of named authors or inventors (not
unnamed contributors), and cannot be used to find who is not named; carefully
curated case studies are too small to generalize   15
.") . The unique data on
research teams used in this paper are, by contrast, fit for the purpose: they
consist of information on 9,778 teams over a four-year period: the 128,859
individuals working in those teams, matched to 39,426 journal articles and
7,675 patents produced by those teams (Methods, ‘Construction of
administrative data’). Because the data include information about the
positions held by each individual on each team as well as their gender, it is
possible to calculate for each individual whether they did or did not receive
credit on a given article and to calculate differences by gender.
*  American Psychological Association 
*  Ovid Technologies, Inc.
We also asked why respondents thought they were not credited: Fig.  4
 (and Supplementary Fig.  7
 ) summarizes the results for the 871
individuals who responded (483 men and 388 women). The most common reason was
that scientific contributions were underestimated, and this was the case for
far more women (48.97%) than men (39.13%) ( _P_ = 0.0036; two-sided _t_ -test;
test value = −2.9218; effect size = −0.0984). Although discrimination or bias
was much less likely to be cited, women were twice as likely (15.46%) to cite
this as a reason than men (7.67%) ( _P_ = 0.0003; two-sided _t_ -test; test
value = −3.6623; effect size = −0.0780). Men were more likely to say that
their contributions did not warrant authorship (37.68% of men compared with
24.74% of women; _P_ = 0.0000; two-sided _t_ -test; test value = 4.1060;
effect size = 0.1294). Differences in responsibilities (that is, a respondent
indicated that they were not granted attribution for at least one of the
following reasons: personal, non-research responsibilities and/or left the
laboratory) appear to account for some of the attribution gap—17.53% of
excluded women cited these reasons, compared with 12.63% of men ( _P_ =
0.0432; two-sided _t_ -test; test value = −2.0244; effect size = −0.0490).
Together, these estimates suggest that a large portion of the gender gap in
attribution is owing to either discrimination or how contributions are
perceived by collaborators, or both.
Some 74% of
women in computer jobs, such as software development or data science, say they
have experienced discrimination because of their gender, compared with 16% of
men in these jobs.  5  (This group includes some who work in the tech industry
and some who work in other sectors.)  6
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###  Hypothesis 3b
Unlike female students, male students are often the beneficiaries of
stereotypes in mathematics or science studies. In general, the idea that “men
are better at mathematics” has a stereotype boost/lift  Footnote  6  effect on
men's mathematics learning, which can promote men’s self-confidence in
learning and their expectation of success (Smith and Johnson  2006
: 51–63.") ). Empirical studies in the United States
and Europe have found that males are more confident in math learning. After
controlling for math performance, males’ evaluation of their mathematical
capability is higher than that of females, and they are more likely to take a
STEM-related career path in the future (Correll  2001
: 1691–1730.") ).
The first and simplest measure is the ever-author rate, which characterizes an
individual as an author if he or she was ever named as an author or an
inventor during the analysis period. As shown in Table  1
 , 16.97% of individuals are classified as
authors using this measure, but the probability that men are ever named is
21.17% whereas the probability for women is 12.15%. Table  1
 also shows that there are two reasons for
this gap: the junior positions of women in research teams, and under-
representation in attribution given their position. First, women are less
likely to be in the senior positions that are associated with ever being named
an author, 'ever authorship'. The highest ever authorship rate (45.70%) is for
faculty members, yet only 11.30% of women (versus 19.72% of men) in the sample
are faculty members. Conversely, the ‘ever authorship’ rate for research staff
is 8.63%, yet 47.81% of women are research staff, compared with 28.73% of men.
Second, holding the distribution of positions constant (at the grand means),
women are 4.82% less likely to ever be named as authors. In the case of
graduate students, for example, 14.97% of women are ever named as an author on
a document compared with 21.37% of men. The consequences of such disparities
on the retention of senior women in and the attraction of young women to
scientific careers are unlikely to be positive.
###  Hypothesis 1b
* Engineering*
We sent a survey to 28,000 scientists who had published in academic journals
listed in the Web of Science and who had a public profile in the ORCID
database. Of these, 2,297 responded and completed the question (Q1a): ‘How did
you contribute to the paper? Check all that apply.’ The graph shows the
percentage of these respondents who selected each category. Probability was
computed as the arithmetic mean of binary indicators representing whether the
respondent selected each category. Each respondent was asked about a paper
associated with them on Web of Science. Respondents were able to select more
than one option, thus the total number of responses is therefore higher than
the number of respondents. Individual data on the contribution by gender are
visualized in Supplementary Fig.  8  .
Error bars are centred on the mean and extend to the 95% confidence interval
based on 1.96 × s.e.m.
###   Supporting statistics for graphs.
**Extended Data Table 8 Survey Response Rates**
report  |  Dec 8, 2023
###  Changeable gender gap in mathematics
By  Cary Funk   and  Kim
Parker
In the Pew
Research Center survey – conducted before the string of prominent sexual
harassment allegations and  public discussion  of these issues on social media outlets
and elsewhere – some 22% of working women in the U.S. say they have
experienced sexual harassment at work, compared with 7% of working men. The
share of women who say they have experienced sexual harassment at work is the
same among those in STEM and non-STEM jobs.  7
[ __ ]() [ __ ]() [ __ ]() [ __ ](javascript:window.print\(\);)
2. Britta M. Glennon
The analysis focuses on a subset of 57 college campuses from 20 universities,
which consistently provided data for the period covering 2013–2016 (refer to
pages 10, 11 and 23 of the UMETRICS summary documentation   36
.") ; Supplementary information, part  2
 ). This restriction ensures that
employment spells are long enough to reasonably identify PIs and teams as well
as to observe the scientific documents produced by those teams from 2014–2016.
The full data include administrative-level information from 392,125 unique
federal and non-federal awards, including 23,307,254 wage payments to 643,463
deidentified individuals   28 .") .
Pew Research
Center analysis of U.S. Census Bureau data since 1990 shows that while jobs in
STEM have grown substantially, particularly in computer occupations, the share
of women working in STEM jobs has remained at about half over time. But the
share of women varies widely across the 74 standard occupations classified as
STEM in this study – from under one-in-ten for sales engineers (7%) and
mechanical engineers (8%) to 96% of speech language pathologists and 95% of
dental hygienists. Women are a majority of those working in health-related
occupations but just 14%, on average, of those in engineering jobs. In
computer occupations, a job cluster which includes computer scientists,
systems analysts, software developers, information systems managers and
programmers – the STEM job cluster that has seen the most growth in recent
decades – women’s representation has actually decreased from 32% in 1990 to
25% today.
The links between UMETRICS and authorship on articles and patents were
generated by combining information on the individual and grants listed
explicitly on the scientific documents as well as the implicit network
structure of co-authorships and grant collaborations. In UMETRICS, the data
include the individual’s name (including partial name in the case of
hyphenated names), the institution and the grant number but, crucially, also
other people on each grant. The same is the case in the publication and patent
data. We identify all patents or articles associated with a given inventor or
author by leveraging PatentsView’s algorithmically assigned inventor ID and
the union of the Web of Science’s researcher ID and the ORCID when they are
available. Key to our approach, these identity clusters enable us to link a
given inventor or author’s full patent and publication history to an
individual’s employee ID in UMETRICS such that we not only see those documents
associated with a specific set of grants or a particular time period, but
their entire patenting and publishing history over their career. The multi-
step procedure, which uses data post 2000, is detailed in Ross et al.   44
.") . There are five steps. The first relies on an exact
match of UMETRICS award numbers to either the award numbers cited in the
government interest field in the patents or the award numbers cited in the
acknowledgement section of the publication. The second step relies on name
matches. It links inventors in Patentsview and authors in Web of Science to
people paid on UMETRICS grants using a sequential process of exact and fuzzy
matching, with matched names removed from the pool for subsequent rounds.
Candidate matches are disqualified for mismatches on institutional affiliation
and dissimilarity of text between awards and publications and patents. The
third step relies on network matches. It uses exact and fuzzy name matching to
find co-inventors (in Patentsview), co-authors (in Web of Science) and
collaborators (in UMETRICS). Candidate matches are disqualified for mismatches
on institutional affiliation and dissimilarity of text between awards and
publications and patents. The fourth step links people by blocked
affiliations. Affiliation names are matched by blocking on the UMETRICS
university affiliation to the affiliations in PatentsView and Web of Science
(using a hand-curated, disambiguated list of university names), and using the
stepwise matching and validation processes described in the second step. As
before, candidate matches are disqualified for mismatches on institutional
affiliation and dissimilarity of text between awards and publications and
patents. The fifth and final step relies on an approximate match of unmatched
grants. It uses the pool of articles or patents associated with the identity
clusters linked in steps 2–4 (namely, employees in UMETRICS linked with their
associated inventor IDs and research ID or ORCID). The restriction that grant
numbers on these documents are deterministically matched is loosened, and a
fuzzy match is allowed between grants in UMETRICS and those unmatched in step
1 but associated with linked individuals.
Article  Open access  07 May 2019
###  Affiliation
These are some of the findings from a Pew Research Center survey with a
nationally representative sample of 4,914 adults (including 2,344 STEM
workers), ages 18 and older, conducted July 11-Aug. 10, 2017 and a Pew
Research Center analysis of U.S. Census Bureau data. The survey, conducted
online in English and in Spanish, included an oversample of employed adults
working in science, technology, engineering and math fields. See  Methodology

for details.
Article   ADS
  CAS
  PubMed

 Google Scholar
Graphs plot the probability that a potential author on a scientific document
(article or patent) is a woman against the probability that an actual author
is a woman. A potential author is defined as an employee in a laboratory
between 2013 and 2016 from which an article or patent was published between
2014 and 2016. There are 17,929,271 potential article authorships and
3,203,831 potential patent inventorships in our sample. The markers in each
panel are sized by the total number of actual authorships in the category. The
diagonal represents parity in the gender composition of potential and actual
authorships. Individual data on potential and actual authorships are shown in
Supplementary Fig.  5  . Left,
disparity across job titles. Right, disparity across research fields.
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.
Subject:  1 selected item: 19883140 - PubMed
Further information on research design is available in the  Nature Research
Reporting Summary  linked to this paper.
* Meta-Analysis
The resulting linkages permit the calculation of the overall ever-author rate,
which is 16.97% overall (12.15% for women and 21.17% for men) (Extended Data
Table  2  ). The attribution rate is
constructed by generating a pool of potential authorships as follows. All
individuals with a faculty job title are considered eligible to be potential
authors on all articles or patents produced by a team during the analysis
period. All individuals with a non-faculty job title had to have been employed
by the team in the year prior to the article of an article or application for
a patent. We relax this time constraint for non-faculty job titles in the
supplement, which generally increases the size of the gender gap reported in
the main estimates.
####  Job titles
The empirical implementation of what is a relatively straightforward
conceptual framework is more difficult, but the data are rich enough to allow
such calculations (see Methods, ‘Analytical sample’ for details). The
denominator—the set of ‘potential authorships’—was created by associating all
members of each team who were employed one year before the publication or
application date to all associated articles or patents emanating from that
team during the analysis period. Since some individuals, such as research
staff, are on multiple teams, they are proportionately allocated across teams
using a set of analytical weights (Methods, ‘Analytical sample’). The
numerator—attribution—was defined as ‘actual authorships’ on those articles
and patents. Thus, the attribution rate is the ratio of actual authorships to
potential authorships. The overall attribution rate for any team member on
either a patent or article is 3.2%. On average across all job titles and
fields, women have a 2.12% probability of being named on any scientific
document, whereas men are twice as likely to be named (4.23%) ( _P_ = 0.0000;
two-sided _t_ -test; test value = 19.5823, effect size = 2.11%; Extended Data
Tables  2  and  3
 ).
A Correction to this paper has been published: 
https://doi.org/10.1038/s41586-023-06571-x
1. Guihua Xie
The average gender stereotype in the class based on the class information in
the seventh grade reflects not only the gender attitude of peer groups toward
mathematics learning (same-gender group effect) but also the stereotype threat
(opposite-gender group effect) faced by the students. From the results of the
model, it can be found that the average gender stereotype score of male
students in the class promotes the mathematical capability of both male and
female students, while the average score of female students has no significant
effect on either male or female students. This shows that female students are
not affected by peer groups (female students) or threatened by gender
stereotypes (male students). The higher the gender bias held by male students
in the class, the higher the female students' mathematical capability score.
To a certain extent, this reproduces the findings of a study many years ago;
that is, the gender bias of male students arouses the “ambition” of female
students (Cui and Venator  2008  ). On the other hand, male students are significantly influenced by
peer groups. The higher the proportion of male students in the class who agree
that male students are better at mathematics than female students, the higher
the score of male students' mathematical capability is. This also confirms the
findings of other researchers that male students are more likely to be
influenced by peer group culture than female students (Legewie and DePrete 
2012 : 463–485.") ), or it may also be because
female students are only affected by close peer groups.
* Aptitude Tests
According to the social psychology literature on the influences of positive
and negative stereotypes and studies on the relationship between stereotypes
and mathematics learning (Spencer et al.  1999
: 4–28.") ), this article
proposes hypotheses on gender stereotypes and mathematics learning from two
perspectives: individual socialization and stereotype threat.
Article  06 October 2022
Empirical research also demonstrates that the gender gap in mathematics
learning is not unalterable, and the panel data show that the gap is
shrinking. In the US, the gender gap in mathematics performance has
disappeared in senior high school, but male students still have advantages in
solving complex problems (Lindberg et al.  2010
: 1123–1135.")
:1132). Research in the United States shows that the proportion of women among
doctoral degree holders in mathematics/ statistics, and physics increased from
8% and 5.5% in 1970 to 32% and 30% in 2006 (Hyde and Mertz  2009
:
8801–8807.") : 8805).
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* Research Support, Non-U.S. Gov't