scholarly journals Perceived Subtle Gender Bias Index: Development and Validation for Use in Academia

2019 ◽  
Vol 43 (4) ◽  
pp. 509-525 ◽  
Author(s):  
Nellie Tran ◽  
Rashelle B. Hayes ◽  
Ivy K. Ho ◽  
Sybil L. Crawford ◽  
Julie Chen ◽  
...  
Keyword(s):  
Gender Bias ◽  
Women In Science ◽  
Gender Inequity ◽  
Bias Index ◽  
Gender Biases ◽  
Index Development ◽  
Academic Faculty ◽  
Current Article ◽  
And Mathematics ◽  
Development And Validation

In this article, we present the development and validation of the Perceived Subtle Gender Bias Index. Given the inherent difficulty in identifying and measuring the perceptions of subtle gender biases, this index provides researchers and interventionists with a tool that does not require participants to identify/label an event as a gender bias incident. We used a mixed method and constructivist approach that prioritized and privileged the voices and experiences of women in science, technology, engineering, and mathematics (STEM). The current article describes two studies: (1) index development and (2) index refinement and validation, using a national survey of women academics ( N = 882). Findings support a four-subscale structure, including perceived gender inequity, collegiality, mentorship, and institutional support. Methods and analyses support face, convergent, discriminant, and predictive validity for the use of the index among academic faculty women. Additional online materials for this article are available on PWQ’s website at http://journals.sagepub.com/doi/suppl/10.1177/0361684319877199

Using Video to Increase Gender Bias Literacy Toward Women in Science

2016 ◽  
Vol 41 (2) ◽  
pp. 175-196 ◽  
Author(s):  
Evava S. Pietri ◽  
Corinne A. Moss-Racusin ◽  
John F. Dovidio ◽  
Dipika Guha ◽  
Gina Roussos ◽  
...  
Keyword(s):  
Gender Bias ◽  
Women In Science ◽  
Mathematics Interventions ◽  
Gender Inequity ◽  
Unfair Treatment ◽  
Science Technology ◽  
Gender Biases ◽  
Expert Interview ◽  
Underrepresentation Of Women ◽  
And Mathematics

Despite evidence that gender biases contribute to the persistent underrepresentation of women in science, technology, engineering, and mathematics, interventions that enhance gender bias literacy about these fields remain rare. The current research tested the effectiveness of two theoretically grounded sets of videos at increasing gender bias literacy as characterized by (a) awareness of bias, (b) knowledge of gender inequity, (c) feelings of efficacy at being able to notice bias, and (d) recognition and confrontation of bias across situations. The narrative videos utilized entertaining stories to illustrate gender bias, while the expert interview videos discussed the same bias during an interview with a psychology professor. The narrative videos increased participants’ immersion in the story and identification with characters, whereas the expert interviews promoted logical thinking and perceptions of being knowledgeable about gender bias facts. Compared with control videos, the narrative and expert interview videos increased awareness of bias (Experiments 1 and 2) and influenced knowledge of gender inequity, self-efficacy beliefs, and the recognition of bias in everyday situations (Experiment 2). However, only the expert interview videos affected participants’ intentions to confront unfair treatment. Additional online materials for this article are available to PWQ subscribers on PWQ’s website at http://pwq.sagepub.com/supplemental

scholarly journals A “Scientific Diversity” Intervention to Reduce Gender Bias in a Sample of Life Scientists

2016 ◽  
Vol 15 (3) ◽  
pp. ar29 ◽  
Author(s):  
Corinne A. Moss-Racusin ◽  
Jojanneke van der Toorn ◽  
John F. Dovidio ◽  
Victoria L. Brescoll ◽  
Mark J. Graham ◽  
...  
Keyword(s):  
Gender Bias ◽  
Life Sciences ◽  
Women In Science ◽  
Summer Institute ◽  
Science Technology ◽  
Gender Biases ◽  
Participation Of Women ◽  
Underrepresentation Of Women ◽  
And Mathematics ◽  
Science Instructors

Mounting experimental evidence suggests that subtle gender biases favoring men contribute to the underrepresentation of women in science, technology, engineering, and mathematics (STEM), including many subfields of the life sciences. However, there are relatively few evaluations of diversity interventions designed to reduce gender biases within the STEM community. Because gender biases distort the meritocratic evaluation and advancement of students, interventions targeting instructors’ biases are particularly needed. We evaluated one such intervention, a workshop called “Scientific Diversity” that was consistent with an established framework guiding the development of diversity interventions designed to reduce biases and was administered to a sample of life science instructors (N = 126) at several sessions of the National Academies Summer Institute for Undergraduate Education held nationwide. Evidence emerged indicating the efficacy of the “Scientific Diversity” workshop, such that participants were more aware of gender bias, expressed less gender bias, and were more willing to engage in actions to reduce gender bias 2 weeks after participating in the intervention compared with 2 weeks before the intervention. Implications for diversity interventions aimed at reducing gender bias and broadening the participation of women in the life sciences are discussed.

scholarly journals From “Her” Problem to “Our” Problem: Using an Individual Lens Versus a Social-Structural Lens to Understand Gender Inequity in STEM

2018 ◽  
Vol 11 (2) ◽  
pp. 267-290 ◽  
Author(s):  
Kathi N. Miner ◽  
Jessica M. Walker ◽  
Mindy E. Bergman ◽  
Vanessa A. Jean ◽  
Adrienne Carter-Sowell ◽  
...  
Keyword(s):  
Gender Equity ◽  
Women In Science ◽  
Gender Inequity ◽  
Individual Level ◽  
Representation Of Women ◽  
Gender Parity ◽  
The Social ◽  
Social Structural ◽  
Societal Level ◽  
And Mathematics

Increasing the representation of women in science, technology, engineering, and mathematics (STEM) is one of our nation's most pressing imperatives. As such, there has been increased lay and scholarly attention given to understanding the causes of women's underrepresentation in such fields. These explanations tend to fall into two main groupings: individual-level (i.e., her) explanations and social-structural (i.e., our) explanations. These two perspectives offer different lenses for illuminating the causes of gender inequity in STEM and point to different mechanisms by which to gain gender parity in STEM fields. In this article, we describe these two lenses and provide three examples of how each lens may differentially explain gender inequity in STEM. We argue that the social-structural lens provides a clearer picture of the causes of gender inequity in STEM, including how gaining gender equity in STEM may best be achieved. We then make a call to industrial/organizational psychologists to take a lead in addressing the societal-level causes of gender inequality in STEM.

scholarly journals ‘It Is Time to Operate Like a Woman’: A Corpus Based Study of Representation of Women in STEM Fields in Social Media

2019 ◽  
Vol 9 (5) ◽  
pp. 217
Author(s):  
Reem Alkhammash
Keyword(s):  
Social Media ◽  
Gender Stereotypes ◽  
Women In Science ◽  
Women In Stem ◽  
Stem Fields ◽  
Representation Of Women ◽  
Gender Biases ◽  
Twitter Users ◽  
And Mathematics ◽  
And Function

This study explores the discourse of women in science, technology, engineering and mathematics or medicine (STEM) fields produced by Twitter users on social media, with a particular focus on language usage and function in this discourse. The exploration of the women in STEM discourse was achieved by collecting a body of tweets using popular hashtags addressing women in STEM from the last week of October 2017. Following a corpus-based approach, this study analyzes the most frequent evaluative adjectives and 4-grams. Results from the analysis of evaluative adjectives show that Twitter users represent women in STEM fields positively by using positive adjectives such as great, amazing, inspirational etc. Furthermore, the analysis of the most frequent 4-grams reveals that Twitter users employ hashtags such as #ilooklikeasurgeon and #womeninSTEM to promote the work of women in STEM fields, show their appreciation of women working and studying in STEM and challenge prevalent gender stereotypes of STEM professions. It was found that the production of women in STEM discourse by most Twitter users has contributed to increasing the strength of women in the STEM community in social media, evidenced by their practices of advocacy, networking and challenging gender biases online. The discourse of women in STEM in social media is an example of discursive activism that focuses on the larger dialogue of women in STEM and highlights dominant forms of sexism and gendered stereotypes of women’s work in male dominated professions.

“Shining Armour”: what Margaret-Ann Armour taught us about equity, diversity, and inclusion and mentorship in the natural sciences

2021 ◽  
pp. 1-6
Author(s):  
Jennifer Dengate ◽  
Annemieke Farenhorst ◽  
Tracey Peter ◽  
Tamara Franz-Odendaal
Keyword(s):  
Gender Bias ◽  
Natural Sciences ◽  
Women Faculty ◽  
Faculty Members ◽  
Women In Science ◽  
Canadian Universities ◽  
Diversity And Inclusion ◽  
Science Technology ◽  
And Mathematics ◽  
Academic Chemistry

In addition to her contributions to the field of chemistry, Dr. Margaret-Ann Armour was the foremother of equity, diversity, and inclusion in the natural sciences in Canada and was an exemplary mentor to many women in science, technology, engineering, and mathematics. Dr. Armour emphasized that, to make progress in natural sciences and engineering fields, we also need to make advancements in workplace EDI. Dr. Armour was among the first to recognize the need to fix gender biased systems and not women. Analyses of the 2017–2018 Faculty Workplace Climate Survey, administered to approximately 700 natural sciences and engineering professors from 13 Canadian universities, supports Dr. Armour’s position. We present a synthesis of the key findings from the survey, which speak to some of the gendered challenges that women faculty members in Canada still face; and discuss the implications of these findings in light of women’s continued lack of access to mentors, with an emphasis on gender bias in mentorship within academic chemistry.

Media representation and gender bias in science

2021 ◽  
Vol 2 (1) ◽  
pp. 7-15
Author(s):  
Catherine Macdonald
Keyword(s):  
Gender Bias ◽  
Gender Stereotypes ◽  
Role Models ◽  
Cultural Beliefs ◽  
White Men ◽  
Media Representations ◽  
Women In Science ◽  
Working Environments ◽  
And Gender ◽  
And Mathematics

Research on gender bias in science has often focused on the effects of gender stereotypes or a lack of female role models on the recruitment and retention of women in science, technology, engineering and mathematics fields, or on the discrimination women scientists face. Systemic bias fuels, and is cyclically reinforced by, media representations of scientists (who are most often presented as white men). While many proposed interventions to address gender inequality in science focus on changing women’s beliefs or behaviour to help them succeed, more inclusive representation of scientists could meaningfully contribute to reshaping the cultural beliefs that act on both genders to deny women opportunities and produce inhospitable learning and working environments.

scholarly journals Quality of evidence revealing subtle gender biases in science is in the eye of the beholder

2015 ◽  
Vol 112 (43) ◽  
pp. 13201-13206 ◽  
Author(s):  
Ian M. Handley ◽  
Elizabeth R. Brown ◽  
Corinne A. Moss-Racusin ◽  
Jessi L. Smith
Keyword(s):  
Gender Bias ◽  
Stem Fields ◽  
Double Blind ◽  
Science Technology ◽  
Gender Biases ◽  
Culture Of Science ◽  
Stem Faculty ◽  
And Mathematics ◽  
Abstract Reporting

Scientists are trained to evaluate and interpret evidence without bias or subjectivity. Thus, growing evidence revealing a gender bias against women—or favoring men—within science, technology, engineering, and mathematics (STEM) settings is provocative and raises questions about the extent to which gender bias may contribute to women’s underrepresentation within STEM fields. To the extent that research illustrating gender bias in STEM is viewed as convincing, the culture of science can begin to address the bias. However, are men and women equally receptive to this type of experimental evidence? This question was tested with three randomized, double-blind experiments—two involving samples from the general public (n = 205 and 303, respectively) and one involving a sample of university STEM and non-STEM faculty (n = 205). In all experiments, participants read an actual journal abstract reporting gender bias in a STEM context (or an altered abstract reporting no gender bias in experiment 3) and evaluated the overall quality of the research. Results across experiments showed that men evaluate the gender-bias research less favorably than women, and, of concern, this gender difference was especially prominent among STEM faculty (experiment 2). These results suggest a relative reluctance among men, especially faculty men within STEM, to accept evidence of gender biases in STEM. This finding is problematic because broadening the participation of underrepresented people in STEM, including women, necessarily requires a widespread willingness (particularly by those in the majority) to acknowledge that bias exists before transformation is possible.

BUILDING A NETWORK TO SUPPORT GIRLS AND WOMEN IN SCIENCE, TECHNOLOGY, ENGINEERING, AND MATHEMATICS

2004 ◽  
Vol 10 (2) ◽  
pp. 161-178 ◽  
Author(s):  
Jacqueline D. Spears ◽  
Ruth A. Dyer ◽  
Suzanne E. Franks ◽  
Beth A. Montelone
Keyword(s):  
Women In Science ◽  
Science Technology ◽  
And Mathematics

scholarly journals Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?

2021 ◽  
Author(s):  
Manjul Gupta ◽  
Carlos M. Parra ◽  
Denis Dennehy
Keyword(s):  
United States ◽  
Cultural Values ◽  
Gender Bias ◽  
The United States ◽  
Compelling Evidence ◽  
Research Attention ◽  
Academic Literature ◽  
Gender Biases ◽  
Significant Research ◽  
And Gender

AbstractOne realm of AI, recommender systems have attracted significant research attention due to concerns about its devastating effects to society’s most vulnerable and marginalised communities. Both media press and academic literature provide compelling evidence that AI-based recommendations help to perpetuate and exacerbate racial and gender biases. Yet, there is limited knowledge about the extent to which individuals might question AI-based recommendations when perceived as biased. To address this gap in knowledge, we investigate the effects of espoused national cultural values on AI questionability, by examining how individuals might question AI-based recommendations due to perceived racial or gender bias. Data collected from 387 survey respondents in the United States indicate that individuals with espoused national cultural values associated to collectivism, masculinity and uncertainty avoidance are more likely to question biased AI-based recommendations. This study advances understanding of how cultural values affect AI questionability due to perceived bias and it contributes to current academic discourse about the need to hold AI accountable.

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