Women in STEM fields

Many scholars and policymakers have noted that the fields of science, technology, engineering, and mathematics (STEM fields) have been predominantly male occupations, with historically low participation among women , from their origin in the age of enlightenment to the present time. STEM professions, like medicine , require higher education or training-especially in mathematics-in all cases.

Since the feminist revolution of the 1970s , the opportunities available to men and women in higher education have become broadly similar in most advanced economies (with some countries, such as Canada, now having more women than men enrolled in post-secondary education). STEM professions on the ground.

Scholars are exploring the various reasons for the continued existence of this gender disparity in STEM fields. Those Who view this disparity as resulting and from Discriminatory strengths sont également seeking ways to redress this disparity Within STEM fields (thesis Typically construed’re well-compensated, high-status occupations with universal appeal career). [1] [2] [3] [4] [5] Some proponents view diversity as an inherent human good, and wish to increase diversity for its own sake, regardless of its historical origin or present cause.

Gender imbalance in STEM fields

Studies suggest That Many factors contribuer to the Attitudes Towards and achievement of young women in mathematics and science , Including encouragement from parents, interactions with mathematics and science teachers, content curriculum, hands-on laboratory experiments, high school achievement in mathematics and science, and Resources available at home. [6] In the United States , research findings are mixed when boys ‘and girls’ attitudes about mathematics and science diverge. Analyzing several nationally representative longitudinal studies , one researcher found some differences in girls ‘and boys’ attitudes toward science in the early secondary school years. [6]

Students’ aspirations to pursue careers in mathematics and science. A report by the US Department of Education found that the gap in the career aspirations of boys and girls in STEM fields exists as early as eighth grade. Among the eighth grade class of 1988, boys were more likely to be as scientists or engineers (9 and 3 percent, respectively), although girls were more likely to boys to aspire to professional, business, or managerial occupations (38 and 20 percent respectively). While male and female high school seniors are equally likely to expect a career in science or mathematics, Male seniors are much more likely than their female counterparts to expect a career in engineering . [7]

Girls begin to lose self-confidence in middle school because they believe that they possess more intelligence in technical fields. [8] The fact that women in spatial analysis, a skillset many engineering professionals deem vital, generates this misconception. [3] Boys are more likely to gain space skills skills outside the classroom because they are culturally encouraged to build and work with their hands. [9] [1] [1] [1] Research and development [10] [11]

A 1996 study of college freshmen by the Higher Education Research Institute shows that men and women differ greatly in their intended fields of study. Of first-time college freshmen in 1996, 20 percent of men and 4 percent of women planned to major in computer science and engineering, while similar Percentages of men and women planned to major in biology or physical science. The differences in the intended majors between male and female first-time freshmen directly relate to the differences in the fields in which men and women earn their degree. At the post-secondary level, women are less likely than men to earn a degree in mathematics, physical science, or computer science and engineering. The exception to this gender imbalance is in the life sciences. [12]

Effects of underrepresentation of women in STEM careers

In Scotland , a large number of women graduate in STEM subjects. This represents a £ 170 million per annum loss to Scotland’s national income. [13]

Men’s and women’s earnings

See also: Gender pay gap

Although female college graduates in the earnings growth of all college graduates in the 1980s, they earned less on average than male college graduates. Some of the differences in salaries are related to women and men. Bachelor’s degree in science and engineering. There is a wage gap between men and women in comparable scientific positions. Among more experienced scientists and engineers, the gender gap in salaries is greater than for recent graduates. [14] Salaries are highest in mathematics, computer science, and engineering, fields in which women are not highly represented. In Australia ,

Representation of women worldwide

UNESCO , among other agencies including the European Commission and the Association of Academies and Societies of Sciences in Asia (AASSA), have been outspoken about the underrepresentation of women in STEM fields globally. [16] [17] [18]

Asia

Proportion of female graduates in science programs in tertiary education in Asia.

A fact sheet published by UNESCO in March 2015 [19], presented with a focus on Asia and the Pacific region. It reports that, worldwide, 30 percent of researchers are women. In these areas, East Asia and the Pacific and South and West Asia had the most uneven balance, with 20 per cent of researchers being women in each of those sub-regions. Meanwhile, Central Asia had 46% of its researchers. The Central Asian countries Azerbaijan and Kazakhstan were the only countries in Asia with women as the majority of their researchers, though in both cases it was by a very small margin. [19]

countries Percentage of researchers who are female
Central Asia 46%
world 30%
South and West Asia 20%
East Asia and the Pacific 20%

Cambodia

As of 2004, 13.9% of students enrolled in science programs in Cambodia were female and 21% of researchers in science, technology and innovation fields were female as of 2002. These statistics were lower than those of other Asian countries such as Malaysia , Mongolia , and South Korea . According to a report on women in STEM in Asian countries, Cambodia’s education system has a long history of male dominance stemming from its male-only Buddhist teaching practices. Starting in 1924, girls were allowed to enroll in school. Bias against women, not only in education but in other aspects of life as well, Especially in the home and in the workplace, according to UNESCO’s A Complex Formula . [16]

Indonesia

UNESCO’s A Complex Formula states That Indonesia ‘s government has-beens Working Toward gender equality, Especially through the Ministry of Education and Culture , aim stereotypes about women’s roles in the workplace Persists. Due to traditional views and societal norms , women struggle to remain in their careers or to move up in the workplace. Substantially more women are enrolled in science-based fields such as pharmacy and biology in mathematics and physics . Within engineering, statistics vary based on the specific engineering discipline; Women make up 78% of chemical engineering students but only 5% of mechanical engineering students. As of 2005, out of 35,564 researchers in science, technology, and engineering, 10,874 or 31% were female. [16]

Malaysia

According to UNESCO, 48.19% of students enrolled in science programs in Malaysia were female as of 2011. This number has grown in 95%. In Malaysia, over 50% of employees in the computer industry, generally male-dominated field within STEM, are women. Malaysian societal norms, as opposed to “outdoor” jobs more fitting for males. Of students enrolled in pharmacy, more than 70% are female, while in engineering only 36% of students are female. Women held 49% of research positions in science, technology and innovation as of 2011. [16]

Mongolia

Mongolia are female. Mongolia is female. Mongolia is female. Mongolia is female. Traditionally, nomadic Mongolia culture was fairly egalitarian, with both women and men raising children, tending livestock, and fighting in battle, which mirrors the relative equality of women and men in Mongolia’s modern day workforce. More females than males pursue higher education and 65% of college graduates in Mongolia are women. However, women earn about 19-30% less than their male counterparts and are perceived by society to be less suited to engineering than men. Thirty percent or less of employees in computer science, construction architecture, And engineering are female in three in four biology students are female. [16]

Nepal

As of 2011, 26.17% of Nepal’s science students were women and 19% of their engineering students were women. In research, women held 7.8% of positions in 2010. These low percentages corresponds with Nepal’s patriarchal societal values. In Nepal, women enter STEM fields That MOST Often enter forestry or medicine, SPECIFICALLY nursing qui est Perceived as a Predominantly female occupation in MOST countries. [16]

South Korea

In 2012, data showed that 30.63% of students enrolled in science programs in South Korea were female. Numbers of male and female students enrolled at most levels of education. Confucian beliefs in the lower socioeconomic value of women. South Korea’s STEM gender gap. In South Korea, the percentage of women in medicine (61.6%) is much higher than the percentage of women in engineering (15.4%) and other more math-based stem fields. In the United States of America, most women working in STEM fields are classified as “non-regular” or temporary employees, Indicating poor job stability. [16] In a study conducted by the University of Glasgow which examined math anxiety and test performance of boys and girls from various countries, researchers found that South Korea had a high sex difference in mathematics scores, with female students scoring lower than and experiencing More math anxiety on math tests than male students. [20]

North America

United States

According to the Census Bureau’s 2009 American Community Survey, women included 48 percent of the US workforce but just 24 percent of workers in STEM fields. Half as many women work in STEM jobs as STEM professions mirrored the overall workforce. This underrepresentation has been fairly consistent over the past decade, even as women’s share of the college-educated workforce has increased. Among STEM jobs, women’s representation has varied over time. While the percentage of women has declined in computer and math jobs, it has risen in other occupations. In 2009, women comprised 27 percent of the workforce and the workforce (the largest of the STEM components), a drop of 3 percent points since 2000. Engineers are the second largest STEM occupational group. Only about one out of every seven engineers is female. [21]

Men are much more likely than women to have a STEM career lookless of educational attainment. Women in STEM fields earn a lot less than men, even after controlling for a wide set of characteristics. On average, men in STEM jobs $ 36.34 per hour while in STEM jobs earn $ 31.11 per hour. [21]

Percentage distribution of probable fields of study among first-time college freshmen, by sex (fall 1996)

Probable major field of study Men Women
Arts and humanities 9.4 10.5
Biology 6.5 7.4
Business 18.1 13.8
Education 6.3 14.2
Engineering 15.2 2.6
Physical Sciences 2.7 2.0
Professional 9.8 20.2
Social Sciences 6.1 11.7
Technical 3.7 1.4
Computer Science 4.3 1.2
Undecided 7.4 8.8
Other 10.5 6.5

[12]

The physical sciences include fields such as astronomy, chemistry, earth science, mathematics, and physics. The professional category includes fields such as architecture and health technologies. Women are more likely to hold jobs that are less prestigious and have lower wages than those held by men. While many prestigious fields such as engineering, chemistry, physics, and computer science are dominated by men, women are the majority in the social sciences and life sciences.

It is important to note that the under-representation of women in STEM fields is even more pronounced for women of color in the US. African American, Hispanic, and Native American women are severely underrepresented. Within academia, these minority women represent less than 1% of all tenure-track positions despite constituting approximately 13% of total US population. [22]

Underrepresentation in STEM-related awards and competitions

In terms of the most prestigious awards in STEM fields, only a small proportion have been awarded to women. Looking at the Nobel prize for instance, out of 199 laurates in Physics, 169 in Chemistry and 207 in Medicine, there were only two female laureates in physics, and in medicine between 1901 and 2014. [23] The Fields Medal , Which was initiated in 1936 and is considered the most prestigious prize in mathematics, by Maryam Mirzakhani from Iran, in 2014 out of a total of 56 medallists. [24] [25]

When it comes to student participation in prestigious competitions in STEM-related fields such as the International Olympiads for instance, data shows that in the 2014 year, female medallists, and more generally female contestants, were underrepresented. For instance, the percentage of female contestants stood at just 4 per cent for informatics, 5 per cent for mathematics and 6 per cent for physics. Findings on female enrollment within STEM disciplines in higher education. [26] [27] [28] [29] [25]

Recent advances in technology

Abbiss states that “the ubiquity of computers in everyday life has seen the breaking down of gender distinctions in preferences for and the use of different applications, particularly in the use of the internet and email.” [30] GENDER AND DEMOCRACY OF GENDER AND TECHNOLOGY: A GENDER AND METHODOLOGY Classes, which declines from grades 10 to 12 and to post-secondary level program options.

Explanations for low representation of women

Many people have attempted to make sense of the relative low numbers of women in STEM fields, leading to the rise of a number of biological, structural, and social-psychological explanations. [31] [32] [33]

Female interest

A meta-analysis that has a preference for working with people. When interests Were classified by RIASEC types (Realistic, Investigative, Artistic, Social, Enterprising, Conventional) Men Showed stronger Realistic and Investigative interests, and women Showed stronger Artistic, Social, and Conventional interests. Sex differences are also found for more specific measures of engineering, science, and mathematics interests. [34]

In their 3-year interview study, Seymour and Hewitt (1997) found that non-STEM academic majors offered better education options and better matched their interests was the most common (46% Areas to non-STEM areas. The second most frequently cited reason given for switching to non-STEM areas was a loss of interest in the women’s chosen STEM majors. Additionally, 38 percent of female students who stayed in STEM majors expressed concerns that there were other academic areas that could be better fit for their interests. Preston’s (2004) survey of 1,688 individuals who had left the sciences also showed that 30 percent of the women endorsed “other fields more interesting” as their reason for leaving. [34]

A review of UK patent applications in 2016 found that the proportion of new inventions registered by women was rising, but that most female inventors were active in stereotypically female fields as “designing arms and make-up”. 94% of inventions in the field of computing, 96% in automotive applications and mining, and 99% in explosives and ammunition, were by men. [35]

Structural explanations

Rossiter offers two possible structural explanations for the low number of women in STEM fields: hierarchical segregation and territorial segregation. She describes “hierarchical segregation” as a “moves up the ladder of power and prestige.” [33] : 33 Rossiter also puts forth the concept of “territorial segregation” or occupational segregation , which is the idea that women “cluster” in certain fields of study. [36] : 34 For example, “women are more likely to teach and do research in the humanities and social sciences than in the natural sciences and engineering”, [36] : 34 and the majority of college women tend to choose majors such as psychology, education, English, performing arts, and nursing. [37] One reason why women tend to form these “clusters” is because of a lack of support in STEM fields where they are outnumbered by men.

Although it has been postulated that more female role models would encourage more women to enter fields dominated by men, [38] and that women’s lack of interest in STEM fields may instead in part stem from stereotypes about Employees and workplaces in STEM fields, to which stereotypes women are disproportionately responsive. [39] [40]

Leaky pipeline

Main article: STEM pipeline

The metaphor of the leaky pipeline has been used to describe how women drop out of STEM fields at all stages of their careers. Statistician Berry Vetter claims that 280 of any 2,000 9th grade boys and 210 of any 2,000 9th grade girls will have taken enough math to pursue a technical career. Of these, 143 of the men and 45 of the women’s major in science in college. Forty-four of these men and 20 of these women will complete their degrees in science. Five of These men and One of These women will go on to obtenir PhDs in science. [36] : 54-55

Research has found that women steer away from STEM fields because they believe they are not qualified for them; The matched mathematics classes. [41] Teachers often give boys more opportunity to figure out the solution to a problem by themselves while telling the girls to follow the rules. [36] : 56 Teachers are also more likely to accept questions from boys while telling girls to wait for their turns. [42] This is partly due to gender expectations that will be active but that girls should be quiet and obedient. [43] Girls also have less laboratory experience because they are given fewer opportunities to gain such experience than are boys. [42] In middle and high school, races dealing with mechanics and computers as well as the more rigorous science and mathematics courses are taken by the male students and also tend to be taught by male teachers. [44] Girls’ lack of opportunities to practice their math and science skills can lead to a loss of self-esteem in their math and science abilities. Such low self-esteem may prevent women and girls from entering science and math fields. Many girls will end up not taking enough math classes to qualify for three-quarters of majors in college. [42] [44] Girls’ lack of opportunities to practice their math and science skills can lead to a loss of self-esteem in their math and science abilities. Such low self-esteem may prevent women and girls from entering science and math fields. Many girls will end up not taking enough math classes to qualify for three-quarters of majors in college. [42] [44] Girls’ lack of opportunities to practice their math and science skills can lead to a loss of self-esteem in their math and science abilities. Such low self-esteem may prevent women and girls from entering science and math fields. Many girls will end up not taking enough math classes to qualify for three-quarters of majors in college. [42]

Schiebinger claims this leakage may be due to discrimination, both overt and covert, faced by women in STEM fields. [36] : 51 The possible reasons behind these decisions are the following: the use of sexually discriminating norms against women, the struggle to balance family and work, the perceptible need to hide pregnancies, and inflexible working conditions. The New England Journal of Medicine suggests that three-quarters of women students and residents are harassed at least once during their medical training. [36] : 51 In engineering and science education, women make up almost 50 percent of non-tenure track lecturer and instructor jobs, but only 10 percent of tenured or tenure-track professors. In addition, The number of female members of the department has not changed for the past 20 years. [42] This lack of women at the highest levels of a profession may be due to the so-called ” glass ceiling “, a posited phenomenon “that keeps minorities and women from rising to the upper rungs of the corporate ladder, Qualifications or achievements. ” [45]Moreover, women who do make it to these high levels may face the difficulties associated with holding a token status. Because they are highly ranked women, they have antagonism from peers and supervisors. [43]However, STEM has changed greatly in the past two decades and any conclusions about their status based on data prior to 2000 are likely to be outdated. In general, they have earned profits in academic science, including remuneration, promotion, and job satisfaction. [46] Recently, Williams and Ceci show that in both experimental hiring simulations and in real-world academic hiring, women appear to be preferred over their male counterparts. [36] : 51 [47] : 1 [48] Williams and Harris, H. (eds.). [36] : 51 [47] : 1 [48] Williams and Harris, H. (eds.). [36] : 51 [47] : 1 [48]

Gender and work

Both men and women who work in nontraditional occupations may encounter discrimination, but the forms and consequences of this discrimination are very different. Although women entering traditionally male professions face negative stereotypes suggesting that they are not real women, these stereotypes do not seem to deter women to the same degree that similar stereotypes may deter men from pursuing nontraditional professions. There is ample historical evidence that women flock to male-identified occupations once opportunities are available. [49] On the other hand, examples of occupations changing from predominantly female to predominantly male are very rare in our history. The few existing cases-such as medicine-suggest that redefinition of the occupations as appropriately masculine is necessary before men will consider joining them. [50]

Although men in the dominant occupations may contend with negative stereotypes about their masculinity, they may also experience certain benefits. Women, especially those in male-dominated occupations, tend to hit a glass ceiling; While men in female dominated occupations may hit a glass escalator. [51] While the glass ceiling can make it difficult for women and minorities to reach the top of an occupation, the glass escalator allows it to excel in a profession that is female dominated. Since STEM fields tend to be male-dominated, it is likely that women will hit the glass ceiling. [52]

Social-psychological explanations

Psychologists have long studied issues related to discrimination, motivation, and performance. In more recent years, social psychologists have examined how certain social-psychological phenomena may apply directly to the STEM fields, and may explain the relative lack of gender diversity within these fields.

Stereotypes and heuristics

A heuristic is a cognitive shortcut that makes people make decisions. [53] Stereotypes, or commonly held beliefs about certain groups, are often used as heuristics when making decisions in social situations. Stereotypes about what a person in a STEM field should look and act like may cause. [54] The stereotypical scientist or individual in another STEM profession is usually thought to be male. [55] This article describes the conceptualization of what a scientist, engineer, or mathematician “should” Look like and may so be overlooked or penalized. The role congruity theory of prejudice states that perceived incongruity between gender stereotypes and the stereotypes associated with a particular role or occupation can result in negative evaluations. [56] [57] [58] In addition, negative stereotypes about women’s quantitative abilities may lead people to devalue their work or discourage these women from continuing in STEM fields. [59] S quantitative abilities may lead people to devalue their work or discourage these women from continuing in STEM fields. [59] S quantitative abilities may lead people to devalue their work or discourage these women from continuing in STEM fields. [59]

Individuals of a particular gender are often perceived to be better suited to particular careers or areas of study than those of the other gender. [60] [61] A study by Gaucher et al. [60] found that job advertisements for male-dominated careers, which are associated with male stereotypes. If individuals are given information about a prospective student’s gender, they may infer that he possesses traits consistent with stereotypes for that gender. [62] Social role theory states that they are expected to display communal qualities. [63] These expectations can influence hiring decisions. [64] Madera et al. [64] found that women tend to be described in more common terms and men in more terms in letters of recommendation. These researchers also found that communal characteristics were negatively related to hiring decisions in academia. [64]

Another female stereotype associated with male dominated roles is that women are more “manly” and not considered to be “real women”, and many females are turned off at the prospect of these jobs because they do not want to appear less feminine To the opposite sex. This is a result of what women should do and what women should do. [65]

Discrimination

Some researchers have demonstrated a general evaluative bias against women. [66] In an audit study in qui They sent email requests to meet to professors in doctoral programs at the top 260 US universities, Researchers found evidence for discrimination contre ethnic minorities and women relative to Caucasian men. [67] While it was impossible to determine whether any particular individual in this study was exhibiting discrimination, since each participant only viewed a request from one potential graduate student, the overall tendency to favor Much an issue. In another study, science faculty were sent the materials of student who was applying for a position at their university. [68] The materials were the same for each participant, but each participant was randomly assigned either a male or a female name. The researchers found that faculty members rated the male candidate as both the candidate and the candidate. [68] Again, it is impossible to say if any of the individual faculty members were acting in a discriminatory fashion, but it is apparent that there is still a widespread bias against women in science fields. Another study by Ceci, Ginther, Kahn, and Williams (2014) reported that, The authors interpreted this to suggest that the underrepresentation of women in the professorial ranks was not solely caused by sexist hiring, promotion, and remuneration. [48] In a subsequent article by Wendy Williams and Stephen Ceci, 872 faculty at 371 institutions and in all fifty states were studied. They found that the faculty strongly preferred to hire an assistant professor who was a woman over an identically-qualified competitor who was a man. Moreover, they show that in the real world of professorial hiring, there has been a similar preference for hiring women dating back to the 1990s. [69] 872 faculty at 371 institutions and in all fifty states were studied. They found that the faculty strongly preferred to hire an assistant professor who was a woman over an identically-qualified competitor who was a man. Moreover, they show that in the real world of professorial hiring, there has been a similar preference for hiring women dating back to the 1990s. [69] 872 faculty at 371 institutions and in all fifty states were studied. They found that the faculty strongly preferred to hire an assistant professor who was a woman over an identically-qualified competitor who was a man. Moreover, they show that in the real world of professorial hiring, there has been a similar preference for hiring women dating back to the 1990s. [69]

Implicit discrimination

In highly competitive STEM fields, the backing and encouragement of a mentor can make a lot of difference in women’s decisions of whether or not to continue Pursuing a career In Their discipline [70] [71] This May be PARTICULARLY true for younger Individuals Who May Face many obstacles early on in their careers. [5] Since these younger individuals are often found to be more established in their discipline for help and guidance, the responsiveness of these potential mentors and their willingness to help is incredibly important. Spectrometry of Stem Cells in STEM Fields, They may still hold biases-conscious or not-affect how they interact with women looking to enter their particular discipline. In this paper, we present the results of a study of the treatment of women with STEM careers.

Stereotype threat

Stereotype threat arises from the fear that one’s actions will confirm a negative stereotype about one’s in-group. This fear creates additional stress, consuming valuable cognitive resources and lowering task performance in the threatened domain. [72] [73] [74] Individuals are likely to stereotype threat whenever they are assessed in a domain for which there exists a negative stereotype about a group to which they belong. Stereotypes of the undergraduate and graduate studies of the academic performance of women and girls in the United States. [33] [59] Laboratory experiments have also found that individuals who identify strongly with some area (eg math) are more likely to have their performance in that area hampered by stereotype than those who identify strongly with the area. [74] This means that it is not possible to disclose the results of the stereotyped domain. [74]Negative stereotypes about girls’ capabilities in mathematics and science drastically lower their performance in mathematics and science. [75] Studies have found that this gender difference in performance disappears if students are told that there are no differences on a particular mathematics test. [33] This is a great opportunity for women to have a successful career.

Not only stereotype threat HAS beens Widely Criticized by on a theoretical basis, [76] [77] goal Several Has Failed Attempts to replicate it’s experimental evidence. [77] [78] [79] [80] The findings in support of the bias . [80] [81]

Black Sheep effect

Main article: Black sheep effect

The Black Sheep Effect of the Black Sheep Effect. [82] [83] [84] [85] However, when an individual in-group members have average or below average qualities, he or she is likely to evaluate them much lower than out-group members with equivalent qualifications. [82] [83] [84] [85] This might be a good job. Than their male colleagues to help younger women who do not display such skills.

Queen Bee effect

The Queen Bee effect is similar to the Black Sheep effect but applies only to women. It explains why higher-status women, particularly in male-dominated professions, may actually be less likely to help other women than their male colleagues might be. [86] [87] The study by Ellemers et al. [87] found that while students in a number of different disciplines did not exhibit any gender differences in work or work satisfaction, faculty members at the same university. What was particularly surprising was that these people were more likely than not to be able to do so. [87] One potential explanation for this finding is that the mobility for a member of a negatively stereotyped group is often accompanied by a social and psychological distancing of oneself from the group. This implies that women who are successful in male-dominated careers do not see their own success as proof of negative stereotypes about women’s quantitative and analytical abilities. [87] Thus, such women may actually play a role in perpetuating, rather than abolishing, these negative stereotypes. This implies that women who are successful in male-dominated careers do not see their own success as proof of negative stereotypes about women’s quantitative and analytical abilities. [87] Thus, such women may actually play a role in perpetuating, rather than abolishing, these negative stereotypes. This implies that women who are successful in male-dominated careers do not see their own success as proof of negative stereotypes about women’s quantitative and analytical abilities. [87] Thus, such women may actually play a role in perpetuating, rather than abolishing, these negative stereotypes.

Education and perception

Women in STEM fields are underrepresented by an estimated 15% of their male counterparts. The percentage of women earning Ph.Ds in STEM fields is below 30%, whereas the ratio of male / female in non-STEM fields is equal. [88] In a study women related the following traits as being feminine and unprofessional: talking socially, laughing, being soft spoken, or undecided. They reported distancing themselves from women as well as women, and those women who reported that they would shy away from talking about what was seen. Their colleagues. [88] It’s these stereotypes and educational differences that lead to the decline of women in STEM fields. According to Thomas Dee, with boys advancing in Math and Science and girls advancing in reading. [89]

Innate vs. Learned skill

Some studies [90] propose the explanation that STEM fields (and especially fields like math and philosophy) are considered by both teachers and students to require more. Combined with a tendency to view women as having less of the required innate abilities, researchers offers this can result in assessing women ‘s qualified for STEM positions.

Strategies for increasing representation of women

There is a multitude of factors that can explain the low representation of women in STEM careers. Anne-Marie Slaughter , the first woman to hold the position of Director of Policy Planning for the United States Department of State , [91] has recently suggested some strategies to the corporate and political environment to support women to fulfill their abilities. The many roles and responsibilities that they undertake. [92] The academic and research environment for women may benefit by using some of the suggestions she has made to help women excel, while maintaining a work-life balance.

Social-psychological interventions

A number of researchers have tested interventions to alleviate stereotype threat for women in situations where their math and science skills are being evaluated. The hope is that by combating stereotype threats, these interventions will boost women’s performance, encouraging them to persist in STEM careers.

One simple intervention is simply educating individuals about the existence of stereotype threat. Researchers found that women who were taught about stereotype threat and how it could negatively impact women’s performance in math performed. These women also performed better than women who were not taught about stereotype threat before they took the math test. [93]

Role models

One of the proposed methods for alleviating stereotype threat is through introducing role models. A test case for a woman who had been diagnosed with a migraine by a female experimenter. [94] Additionally, these researchers found that this was not the physical presence of the female experimenter. [94] The findings of another study suggest that role models can not be drawn from peer groups. This study found that women in mixed-gender groups were more likely to be female than men. [95] This was the first step in the development of gender equality in women’s empowerment. [95] Similarly, other experiments show that the groups perform salient helped buffer women against stereotype threat. Female participants who read about successful women, even though these successes were not directly related to performance in math, performed better on a subsequent math test than participants who read about successful corporations rather than successful women. [96] A study investigating the role of textbook images on science performance found that women demonstrated better understanding of a chemistry lesson when the text was accompanied by a counter-stereotypic image (ie, of a female scientist) than when the text Was accompanied by a stereotypic image (ie, of a male scientist). [55] Other scholars distinguished between the challenges of both recruitment and retention in increasing women’s participation in STEM fields. These researchers suggest that both female and male role models can be effective in recruiting women to STEM fields, female role models are more effective in promoting the retention of women in these fields. [97] Of a female scientist), which was accompanied by a stereotypic image (ie, of a male scientist). [55] Other scholars distinguished between the challenges of both recruitment and retention in increasing women’s participation in STEM fields. These researchers suggest that both female and male role models can be effective in recruiting women to STEM fields, female role models are more effective in promoting the retention of women in these fields. [97] Of a female scientist), which was accompanied by a stereotypic image (ie, of a male scientist). [55] Other scholars distinguished between the challenges of both recruitment and retention in increasing women’s participation in STEM fields. These researchers suggest that both female and male role models can be effective in recruiting women to STEM fields, female role models are more effective in promoting the retention of women in these fields. [97] Female role models are more effective in promoting the retention of women in these fields. [97] Female role models are more effective in promoting the retention of women in these fields. [97]

Self-affirmation 

Researchers have investigated the usefulness of self-affirmation in alleviating stereotype threat. One of the most important findings of this study is the lack of a stereotype. [98] A college students who were enrolled in an introductory physics course, wrote about their most important values. [99] Scholars believe that the effectiveness of such values-assertion exercises is their ability to help individuals themselves as individuals, rather than through the lens of a harmful stereotype. Supporting this hypothesis, Self-concept maps with many nodes did not experience a performance on a math test. [100] However, women who did not draw self-concept maps or only drew maps with a few nodes did. [100] The effect of these maps with many nodes was to remind women of their “multiple roles and identities,” which were unrelated to, and would therefore not be harmed by, their performance on the math test. [100] Women who did not draw self-concept maps or only drew maps with a few nodes did. [100] The effect of these maps with many nodes was to remind women of their “multiple roles and identities,” which were unrelated to, and would therefore not be harmed by, their performance on the math test. [100] Women who did not draw self-concept maps or only drew maps with a few nodes did. [100] The effect of these maps with many nodes was to remind women of their “multiple roles and identities,” which were unrelated to, and would therefore not be harmed by, their performance on the math test. [100]

Organized efforts

Organizations such as Girls Who Code , StemBox [101] , Blossom, Engineer Girl, and Kode with Klossy (spearheaded by supermodel Karlie Kloss ) Many of these organizations offer summer programs and scholarships to girls interested in STEM fields. The US government has funded similar endeavors; The Department of State’s Office of Educational and Cultural Affairs. [102]

See also

  • Sex and intelligence
  • Women in science
  • Association for Women in Science
  • Association for Women in Mathematics
  • Stereotype threat
  • Pygmalion effect
  • Black sheep effect
  • Beyond Bias and Barriers
  • Implicit stereotypes
  • Glass ceiling
  • Inequality in the workplace
  • STEM fields
  • Heuristics in judgment and decision making
  • Category: Organizations for women in science and technology
  • Margaret W. Rossiter History of Women in Science Prize
  • Women in science

Sources

References

Notes

  1. Jump up^ Gürer, Denise and Camp, Tracy (2001). Investigating the Incredible Shrinking Pipeline for Women in Computer Science.Final Report – NSF Project 9812016.
  2. Jump up^ This, SJ; Williams, WM (2010). “Sex Differences in Math-Intensive Fields”. Current Directions in Psychological Science . 19(5): 275-279. Doi : 10.1177 / 0963721410383241 .
  3. ^ Jump up to:a b This, SJ; Williams, WM; Barnett, SM (2009). “Women’s underrepresentation in science: Sociocultural and biological considerations”. Psychological Bulletin . 135 (2): 218-261. PMID 19254079 . Doi : 10.1037 / a0014412 .
  4. Jump up^ Diekman, AB; Brown, ER; Johnston, AM; Clark, EK (2010). “Seeking Congruity Between Goals and Roles”. Psychological Science . 21 (8): 1051-1057. PMID 20631322 . Doi : 10.1177 / 0956797610377342 .
  5. ^ Jump up to:a b Griffith, AL (2010). “Persistence of women and minorities in STEM field majors: Is it the school that matters?”. Economics of Education Review . 29 (6): 911-922. Doi : 10.1016 / j.econedurev.2010.06.010 .
  6. ^ Jump up to:a b S. L. Hanson, “Lost Talent, Women in the Sciences”, Philadelphia, Pa .: Temple University Press, 1996.
  7. Jump up^ US Department of Education, National Center for Education Statistics, A Profile of the American Eighth-Grader: NELS: 88 Student Descriptive Summary, Washington, DC: 1990, Table 4.6
  8. Jump up^ Pajares, F (1996). “Self-efficacy beliefs and mathematical problem-solving of gifted students”. Contemporary Educational Psychology . 21 (4): 325-44. Doi : 10.1006 / ceps.1996.0025 .
  9. Jump up^ Hill, Catherine, and Christianne Corbett. Why so Few? Women in Science, Technology, Engineering, and Mathematics. Washington, DC: AAUW, 2010.
  10. Jump up^ Sorby, SA (2009). “Educational research in developing 3-D spatial skills for engineering students”. International Journal of Science Education . 31 (3): 459-80. Doi : 10.1080 / 09500690802595839 .
  11. Jump up^ “Stem Talent Girl: to empower girls and women to ensure they develop their talent” . 2017.
  12. ^ Jump up to:a b Higher Education Research Institute, Graduate School of Education and Information Studies, The American Freshman: National Norms for Fall 1996, University of California, Los Angeles, 1996.
  13. Jump up^ Tapping All our Talents , United Kingdom: The Royal Society of Edinburgh, April 2012, ISBN 9780902198661 , retrieved 4 March 2015
  14. Jump up^ National Science Foundation, Women, Minorities and Persons with Disabilities in Science and Engineering: 1996, Washington, DC: 1996, appendix table 5-8.
  15. Jump up^ “Women in STEM in Australia” (PDF) . Professionals Australia.
  16. ^ Jump up to:a b c d e f g “A Complex Formula: Girls and Women in Science, Technology, Engineering and Mathematics in Asia” (PDF) . UNESCO . UNESCO Bangkok Office. 2015 . Retrieved 29 October2016 .
  17. Jump up^ “She figures 2012 – Research policy and organization – EU Bookshop” . Doi : 10.2777 / 38520 .
  18. Jump up^ “Women in Science and Technology in Asia” . The InterAcademy Partnership . AASSA, Gyeonggi-Do. 1 September 2015 . Retrieved 29 October 2016 .
  19. ^ Jump up to:a b “Women in Science” (PDF) . UNESCO.
  20. Jump up^ Stoet, Gijsbert; Bailey, Drew H .; Moore, Alex M .; Geary, David C. (2016-04-21). “Countries with Higher Levels of Gender Equality Show Larger National Sex Differences in Mathematics Anxiety and Relatively Lower Parental Mathematics Valuation for Girls” . PLOS ONE . 11 (4): e0153857. ISSN 1932-6203 . PMC 4839696  . PMID 27100631 . Doi : 10.1371 / journal.pone.0153857 .
  21. ^ Jump up to:a b Beede, David, Tiffany Julian, David Langdon, George McKittrick, Beethika Khan, and Mark Doms. US Department of Commerce Economics and Statistics Administrations. www.esa.doc.gov. “Women in STEM: A Gender Gap to Innovation”, 2009.
  22. Jump up^ Towns, Marcy (Spring 2010). “Where Are the Women of Color?” On the African American, Hispanic and Native American Faculty in STEM (PDF) . National Science Teachers Association .
  23. Jump up^ Nobelprize.org. 2014.Nobel Prize Facts. Http://www.nobelprize.org/nobel_prizes/facts/ (Accessed 29 October, 2014)
  24. Jump up^ IMU. 2014.The Work of Maryam Mirzakhani. Press Release. Http://www.mathunion.org/fileadmin/IMU/Prizes/2014/news_release_mirzakhani.pdf (Accessed 30 September, 2014)
  25. ^ Jump up to:a b UNESCO (2015). A Complex Formula: Girls and Women in Science, Technology, Engineering and Mathematics in Asia(PDF) . Paris, UNESCO. pp. 15, 23-24. ISBN 978-92-9223-492-8 .
  26. Jump up^ IBO. 2014.IBO 2014 Results. IBO 2014: http: // ibo2014.org/international-biology-olympiad/ results / (Accessed 22 September, 2014)
  27. Jump up^ IOI. 2014.IOI 2014 Contestants. International Olympiad in Informatics – Statistics: http://stats.ioinformatics.org/contestants/2014 (Accessed 23 September, 2014)
  28. Jump up^ IPhO. 2014.Results. IPHO 2014: http://ipho2014.kz/blogs/view/1/42 (Accessed 23 September, 2014)
  29. Jump up^ IMO. 2014. 55th IMO 2014.Individual Results. International Mathematical Olympiad: http://www.imo-official.org/year_individual_ r.aspx? Year = 2014 (Accessed 23 September, 2014)
  30. Jump up^ Abbiss, Jane (2011). “Boys and Machines” (PDF) . Gender and Education . 23 (5): 601-617. Doi : 10.1080 / 09540253.2010.549108 .
  31. Jump up^ Gender Gap in Spatial Ability Can Be Reduced Through Training,Science Daily, 16 September 2010
  32. Jump up^ Has feminism changed science? Londa Schiebinger Cambridge: Harvard University Press, 1999 p. 33
  33. ^ Jump up to:a b c Spencer, SJ; Steele, CM; Quinn, DM (1999). “Stereotype threat and women’s math performance”. Journal of Experimental Social Psychology . 35 (1): 4-28. Doi : 10.1006 / jesp.1998.1373 .
  34. ^ Jump up to:a b Su, Rong; Rounds, James; Armstrong, Patrick (2009). “Men and Things, Women and People: A Meta-Analysis of Sex Differences in Interests”. Psychological Bulletin . 135 (6): 859-884. PMID 19883140 . Doi : 10.1037 / a0017364 .
  35. Jump up^ Keate, Georgia (December 27, 2016). “New generation of inventors wanted: women need to apply”. The Times . pp. 22-23.
  36. ^ Jump up to:a b c d e f g h Schiebinger, Londa (1999). Has Feminism Changed Science? . Harvard University Press.
  37. Jump up^ Ruchika Tulshyan. “Top 10 College Majors for Women – 10: Liberal Arts and Sciences, General Studies, Humanities” . Forbes.com. Archived from the original on 2013-01-23 . Retrieved 2013-03-07 .
  38. Jump up^ Cheryan, Sapna; Siy, John Oliver; Vichayapai, Marissa; Drury, Benjamin J .; Kim, Saenam. “Do Female and Male Role Models Who Embody STEM Stereotypes Hinder Women’s Anticipated Success in STEM?” . Social, Psychological, and Personality Science, via Sage Journals . Retrieved 27 July 2015 .
  39. Jump up^ Page, Lewis (15 December 2009). “Ladies put off tech careers by sci-fi posters, Coke cans” . Retrieved 27 July 2015 .
  40. Jump up^ Page, Lewis (27 June 2013). “Trick-cyclist’s claim: I have FOUND how to get GIRLS INTO TECH” . Retrieved 27 July 2015.
  41. Jump up^ Page, Lewis (27 July 2015). “It’s NOT the Men’s Fault” . Retrieved 27 July 2015 .
  42. ^ Jump up to:a b c d Pell, AN “Fixing the leaky pipeline: women scientists in academia.” (PDF) . Journal of Animal Science . Retrieved 4 December 2012 .
  43. ^ Jump up to:a b Lips, Hilary (2008). Sex & Gender: An Introduction Sixth Edition . New York: McGraw Hill.
  44. Jump up^ http://www.msnbc.msn.com/id/50056290/ns/local_news-phoenix_az/t/popovich-girls-need-be-shown-path-success-through-stem-education/#.UL2VYYNfCSo . Retrieved December 5, 2012 . Missing or empty( help ) [ dead link ] |title=
  45. Jump up^ Federal Glass Ceiling Commission. Solid Investments: Making Full Use of the Nation’s Human Capital . Washington, DC: US Department of Labor, November 1995, p. 4.
  46. Jump up^ Ceci, Stephen (2014). Psychological Science in the Public Interest. Association for Psychological Science. p. 76.
  47. Jump up^ Williams, Wendy (2015). National hiring experiments . Proceedings of the National Academy of Sciences.
  48. ^ Jump up to:a b This, SJ; Ginther, DK; Kahn, S .; Williams, WM (2014). “Women in academic science: a changing landscape”. Psychological Science in the Public Interest . 15 (3): 75-141. PMID 26172066 . Doi : 10.1177 / 1529100614541236 .
  49. Jump up^ Cohn, Samuel. 1985.The Process of Occupational Sex-Typing. Philadelphia: Temple University Press.
  50. Jump up^ Ehrenreich, Barbara, and Deirdre English. 1978. For Her Own Good: 100 Years of Expert Advice to Women. Garden City, NY: Anchor Press.
  51. Jump up^ Williams, Christine (1992). “The Glass Escalator: Hidden Advantages for Men in the ‘Female’ Professions”. Social Problems. 39 : 253-267. JSTOR 3096961 . Doi : 10.1525 / sp.1992.39.3.03x0034h .
  52. Jump up^ Reskin, Barbara, and Patricia Roos.1990. Job Queues, Gender Queues: Explaining Women’s Inroads into Male Occupations. Philadelphia: Temple University Press.
  53. Jump up^ Tversky, Amos; Kahneman, Daniel (1973). “Availability: A heuristic for judging frequency and probability”. Cognitive Psychology . 5 (2): 207-232. Doi : 10.1016 / 0010-0285 (73) 90033-9 .
  54. Jump up^ Wells, Gary L. (2007). “The Conjunction Error and the Representativeness Heuristic”. Social Cognition . 3 (3): 266-279. Doi : 10.1521 / soco.1985.3.3.266 .
  55. ^ Jump up to:a b Good, Jessica J .; Woodzicka, Julie A .; Wingfield, Lylan C. (2010). “The Effects of Gender Stereotyping and Counter-Stereotypic Textbook Images on Science Performance”. Journal of Social Psychology . 150 (2): 132-147. Doi : 10.1080 / 00224540903366552 .
  56. Jump up^ Eagly, AH; Karau, SJ (2002). “Role congruity theory of prejudice to female leaders”. Psychological Review . 109 (3): 573-598. PMID 12088246 . Doi : 10.1037 / 0033-295x.109.3.573 .
  57. Jump up^ Garcia-Retamero, R .; Lopez-Zafra, E. (2006). “Prejudice against Women in Male-congenial Environments: Perceptions of Gender Role Congruity in Leadership”. Sex Roles . 55 (1-2): 51-61. Doi : 10.1007 / s11199-006-9068-1 .
  58. Jump up^ Ritter, BA; Yoder, JD (2004). “Gender Differences in Leader Emergence Persist Even for Dominant Women: An Updated Confirmation of Role Congruity Theory”. Psychology of Women Quarterly . 28 (3): 187-193. Doi : 10.1111 / j.1471-6402.2004.00135.x .
  59. ^ Jump up to:a b Miyake, A .; Kost-Smith, LE; Finkelstein, ND; Pollock, SJ; Cohen, GL; Ito, TA (2010). “Reducing the Gender Achievement Gap in College Science: A Classroom Study of Values ​​Affirmation”. Science . 330 (6008): 1234-1237. PMID 21109670 . Doi : 10.1126 / science.1195996 .
  60. ^ Jump up to:a b Left, D .; Friesen, J .; Kay, AC (2011). “Evidence that gendered wording in job advertisements exists and sustains gender inequality”. Journal of Personality and Social Psychology . 101 (1): 109-128. PMID 21381851 . Doi : 10.1037 / a0022530 .
  61. Jump up^ Lyness, KS; Heilman, ME (2006). “When it is fundamental: Performance evaluations and promotions of upper-level female and male managers”. Journal of Applied Psychology . 91 (4): 777-785. PMID 16834505 . Doi : 10.1037 / 0021-9010.91.4.777 .
  62. Jump up^ Deaux, K .; Lewis, LL (1984). “Structure of gender stereotypes: Interrelationships among components and gender label”. Journal of Personality and Social Psychology . 46 (5): 991-1004. Doi :10.1037 / 0022-3514.46.5.991 .
  63. Jump up^ Eagly, AH; Wood, W. (1991). “Explaining Sex Differences in Social Behavior: A Meta-Analytic Perspective”. Personality and Social Psychology Bulletin . 17 (3): 306-315. Doi : 10.1177 / 0146167291173011 .
  64. ^ Jump up to:a b c Madera, JM; Hebl, MR; Martin, RC (2009). “Gender and letters of recommendation for academia: Agentic and communal differences”. Journal Applied Psychology . 94 (6): 1591-1599. Doi: 10.1037 / a0016539 .
  65. Jump up^ Collins, Rebecca L. (2011-01-22). “Content Analysis of Gender Roles in Media: Where Are We Now and Where Should We Go?”. Sex Roles . 64 (3-4): 290-298. ISSN 0360-0025 . Doi :10.1007 / s11199-010-9929-5 .
  66. Jump up^ Swim, J .; Borgida, E .; Maruyama, G .; Myers, DG (1989). “Jo McKay versus John McKay: Do gender stereotypes bias evaluations?”. Psychological Bulletin . 105 (3): 409-429. Doi :10.1037 / 0033-2909.105.3.409 .
  67. Jump up^ Milkman, KL; Akinola, M .; Chugh, D. (2012). “Temporal Distance and Discrimination: An Audit Study in Academia”. Psychological Science . 23 (7): 710-717. PMID 22614463 . Doi : 10.1177 / 0956797611434539 .
  68. ^ Jump up to:a b Moss-Racusin, CA; Dovidio, JF; Brescoll, VL; Graham, M .; Handelsman, J. (2012). “Science faculty’s subtle gender biases favor male students” . Proceedings of the National Academy of Sciences . 109 : 16474-16479. PMC 3478626  . PMID 22988126 . Doi : 10.1073 / pnas.1211286109 .
  69. Jump up^ Williams, WJ; This, SJ (2015). “National hiring experiments reveals 2-to-1 preference for women faculty on STEM tenure-track.” . Proceedings of the National Academy of Sciences . 1 12 (17): 5360-5365. PMC 4418903  . PMID 25870272 . Doi :10.1073 / pnas.1418878112 .
  70. Jump up^ Sonnert, G .; Fox, MF; Adkins, K. (2007). “Undergraduate Women in Science and Engineering: Effects of Faculty, Fields, and Institutions Over Time”. Social Science Quarterly . 88 (5): 1333-1356. Doi : 10.1111 / j.1540-6237.2007.00505.x .
  71. Jump up^ Stout, JG; Dasgupta, N .; Hunsinger, M .; McManus, MA (2011). “STEMING THE TIDE: Using ingroup experts to inoculate women’s self-concept in science, technology, engineering, and mathematics (STEM)”. Journal of Personality and Social Psychology . 100 (2): 255-270. PMID 21142376 . Doi : 10.1037 / a0021385 .
  72. Jump up^ Schmader, T .; Johns, M. (2003). “Converging evidence that stereotype threat reduces working memory capacity”. Journal of Personality and Social Psychology . 85 (3): 440-452. PMID 14498781 . Doi : 10.1037 / 0022-3514.85.3.440 .
  73. Jump up^ Steele, CM; Aronson, J. (1995). “Stereotype Threat and the Intellectual Test Performance of African Americans”. Journal of Personality and Social Psychology . 69 (5): 797-811. PMID 7473032 . Doi : 10.1037 / 0022-3514.69.5.797 .
  74. ^ Jump up to:a b c Steele, CM; Spencer, SJ; Aronson, J. (2002). “Contending with group image: The psychology of stereotype and social identity threat”. Advances in Experimental Social Psychology . 34 : 379-440. Doi : 10.1016 / s0065-2601 (02) 80009-0 .
  75. Jump up^ Christine Bork,Women in science, Huffington Post
  76. Jump up^ Arthur Robert Jensen “The g Factor: the science of mental ability” 1998ISBN 0-275-96103-6, Praeger Publishers, 88 Post Road West, Westport, CT 06881, pages 513-515: “the phenomenon of stereotype Threat Can Be Explained in terms of a more general construct, test anxiety, qui has-been Studied since the early days of psychometrics. test anxiety tend to lower performance levels we test in proportion to the degree of complexity and the amount of mental effort they require of the subject. The Relatively Greater effect of test anxiety in the black samples, Who HAD Somewhat lower SAT scores than the white subjects in the Stanford experiments deriving their year example of the Yerkes-Dodson law …
  77. ^ Jump up to:a b Stoet, G .; Geary, DC (2012). “Can stereotype threat explain the gender gap in mathematics performance and achievement?”. Review of General Psychology . 16 : 93-102. Doi : 10.1037 / a0026617 . Pdf.
  78. Jump up^ Fryer, RG; Levitt, SD; List, JA (2008). “Exploring the Impact of Financial Incentives on Stereotype Threat: Evidence from a Pilot Study”. American Economic Review . 98 (2): 370-375. Doi :10.1257 / aer.98.2.370 .
  79. Jump up^ Yong, Ed (September 9, 2016). “A Worrying Trend for Psychology’s ‘Simple Little Tricks ‘ ” . The Atlantic . Retrieved 11 September 2016 .
  80. ^ Jump up to:a b Ganley, Colleen M .; Mingle, Leigh A .; Ryan, Allison M .; Ryan, Katherine; Vasilyeva, Marina; Perry, Michelle (1 January 2013). “An Examination of Stereotype Threat Effects on Girls’ Mathematics Performance.” (PDF) . Developmental Psychology . 49 : 1886-1897. PMID 23356523 . Doi : 10.1037 / a0031412.
  81. Jump up^ Flore, Paulette C .; Wicherts, Jelte M. (2014). “Does stereotype threat influence the performance of girls in stereotyped domains? A meta-analysis”. Journal of School Psychology . 53 (1): 25-44. ISSN 0022-4405 . PMID 25636259 . Doi : 10.1016 / j.jsp.2014.10.002 .
  82. ^ Jump up to:a b Eidelman, S .; Biernat, M. (2003). “Derogating black sheep: Individual or group protection?”. Journal of Experimental Social Psychology . 39 (6): 602-609. Doi : 10.1016 / s0022-1031 (03) 00042-8 .
  83. ^ Jump up to:a b Kerr, NL; Hymes, RW; Anderson, AB; Weathers, JE (1995). “Defendant-juror similarity and mock juror judgments”. Law and Human Behavior . 19 (6): 545-567. Doi : 10.1007 / bf01499374.
  84. ^ Jump up to:a b Marques, J .; Abrams, D .; Serodio, RG (2001). “Being better by being right: Subjective group dynamics and derogation of in-group deviants when generic norms are undermined”. Journal of Personality and Social Psychology . 81 (3): 436-447. Doi : 10.1037 / 0022-3514.81.3.436 .
  85. ^ Jump up to:a b Taylor, TS; Hosch, HM (2004). “An examination of jury verdicts for a similarity-leniency effect, an out-group punitiveness effect or a black sheep effect”. Law and Human Behavior . 28 (5): 587-598. Doi : 10.1023 / b: lahu.0000046436.36228.71 .
  86. Jump up^ Cooper, VW (1997). “Homophily or the Queen Bee Syndrome”. Small Group Research . 28 (4): 483-499. Doi : 10.1177 / 1046496497284001 .
  87. ^ Jump up to:a b c d Ellemers, N .; Van den Heuvel, H .; Of Gilder, D .; Maass, A .; Bonvini, A. (2004). “The underrepresentation of women in science: Differential commitment or the queen bee syndrome?”. British Journal of Social Psychology . 43 (3): 315-338. PMID 15479533 . Doi : 10.1348 / 0144666042037999 .
  88. ^ Jump up to:a b Miyake, Akira, et al. “Reducing the Gender Achievement Gap in College Science: A Classroom Study of Values ​​Affirmation.” Science, vol. 330, no. 6008, 2010, pp. 1234-1237. New Series, www.jstor.org/stable/40931525.
  89. Jump up^ Dee, Thomas S. “Teachers and the Gender Gaps in Student Achievement.” The Journal of Human Resources, vol. 42, no. 3, 2007, p. 528-554., Www.jstor.org/stable/40057317.
  90. Jump up^ Beliefs about innate talent may dissuade students from STEM
  91. Jump up^ Slaughter, A
  92. Jump up^ Finding a work-life-balance, Toronto Star , 11 July 2012
  93. Jump up^ Johns, Michael; Schmader, Toni; Martens, Andy (2005). “Knowing Is Half the Battle: Teaching Stereotype Threat as a Means of Improving Women’s Math Performance”. Psychological Science . 16 (3): 175-179. PMID 15733195 . Doi : 10.1111 / j.0956-7976.2005.00799.x .
  94. Jump up to:a b Marx, DM; Roman, JS (2002). “Female role models: Protecting women’s math performance”. Personality and Social bulletin . 28 (9): 1183-1193. Doi : 10.1177 / 01461672022812004 .
  95. ^ Jump up to:a b Huguet, P .; Regner, I. (2007). “Stereotype threat among schoolgirls in quasi-ordinary classroom circumstances”. Journal of Educational Psychology . 99 (3): 545-560. Doi : 10.1037 / 0022-0663.99.3.545 .
  96. Jump up^ McIntyre, RB; Paulson, RM; Lord, CG (2003). “Alleviating Women’s Mathematics Stereotype Threat Through Salience of Group Achievements”. Journal of Experimental Social Psychology. 39 (1): 83-90. Doi : 10.1016 / s0022-1031 (02) 00513-9 .
  97. Jump up^ Drury, Benjamin J .; Siy, John Oliver; Cheryan, Sapna (2011). “The Importance of Differentiating Recruitment from Retention in STEM”. Psychological Inquiry . 22 (4): 265-269. Doi : 10.1080 / 1047840x.2011.620935 .
  98. Jump up^ Martens, A .; Johns, M .; Greenberg, J .; Schimel, J. (2006). “Combating stereotype threat: The effect of self-affirmation on women’s intellectual performance”. Journal of Experimental Social Psychology . 42 (2): 236-243. Doi : 10.1016 / j.jesp.2005.04.010 .
  99. Jump up^ Miyake, A .; Kost-Smith, LE; Finkelstein, ND; Pollock, SJ; Cohen, GL; Ito, TA (2010). “Reducing the Gender Achievement Gap in College Science: A Classroom Study of Values ​​Affirmation”. Science . 330 (6008): 1234-1237. PMID  21109670 . Doi :10.1126 / science.1195996 .
  100. ^ Jump up to:a b c Gresky, DM; Eyck, LLT; Lord, CG; McIntyre, RB (2005). “Effects of salient multiple identities on women’s performance under mathematics stereotype threat”. Sex Roles . 53 (9-10): 703-716. Doi : 10.1007 / s11199-005-7735-2 .
  101. Jump up^ “Introducing StemBox, Birchbox’s Super Smart Little Sister” . Retrieved 2015-07-22 .
  102. Jump up^ “Advancing the Status of Women and Girls Around the World”. Retrieved 2016-09-25 .

Start a Conversation

Your email address will not be published. Required fields are marked *