Identifying and Reducing Gender Bias in STEM: Systematically Synthesizing the Experimental Evidence

识别和减少 STEM 中的性别偏见:系统地综合实验证据

基本信息

项目摘要

This project will integrate high-quality experimental evidence on the existence of, and strategies to reduce, gender bias in science, technology, engineering, and mathematics (STEM) fields, including how gender bias may intersect with other identities such as race and ethnicity. Biases favoring men could thwart women’s training and careers in STEM fields in many ways, but research also suggests promising interventions for changing biased cultures and structures. This project will synthesize four decades of research to understand the postsecondary and workforce contexts in which bias against women in STEM remains especially pernicious and the interventions that can most effectively reduce such biases. The results will inform scholarly debates such as whether biases against women in STEM have reduced over time, persist in nearly all training and career contexts, or vary in more nuanced ways across contexts and STEM fields. The work ultimately aims to help organizations (a) disrupt the culture of peer and mentor discrimination that could directly block women’s entry into STEM fields and (b) mitigate the accumulated experiences of discrimination and exclusion that could drive women out of STEM. Dissemination of project findings will especially focus on actionable insights for higher education institutions, such as bias reduction strategies that male and female STEM faculty can adopt in their teaching, mentoring, and service activities. This project is funded by the EHR Core Research (ECR) program, which supports work that advances fundamental research on STEM learning and learning environments, broadening participation in STEM, and STEM workforce development.The synthesis will include two sets of studies: (a) studies testing for the existence of gender bias in STEM fields (bias identification studies) and (b) studies evaluating interventions to reduce such biases (bias reduction studies). For both, the synthesis will focus on randomized experimental designs, such as changing the name on a résumé from John to Jennifer (bias identification study) or assigning some individuals to receive diversity training or not (bias reduction study). Focusing on experimental designs maximizes the rigor of the evidence to be synthesized because they help rule out potential confounds when testing for bias and evaluating intervention efficacy. All STEM fields will be eligible for review, spanning undergraduate education to the academic and nonacademic workforce, to provide a robust and thorough understanding of where gender biases exist and how interventions can reduce them. Contrasting with traditional literature reviews, the team will use rigorous systematic review and meta-analysis methods to improve transparency of the review process, reduce reviewer bias, and ensure the project’s findings are robust and comprehensive of existing high-quality evidence. Statistical analyses will focus on understanding how specific contextual features (e.g., formal accountability, disciplinary field, intervention design) can explain mixed findings on identifying and reducing gender bias in STEM. This knowledge can help universities, companies, and other organizations pinpoint where targeted intervention is most needed and which strategies will be most effective for mitigating bias.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
该项目将整合高质量的实验证据,以减少科学,技术,工程和数学(STEM)领域的性别偏见,包括性别偏见如何与种族和民族等其他身份交叉。偏袒男性的偏见可能会在许多方面阻碍女性在STEM领域的培训和职业生涯,但研究也表明,有希望的干预措施可以改变有偏见的文化和结构。该项目将综合四十年的研究,以了解中学后和劳动力环境,其中对STEM中女性的偏见仍然特别有害,以及可以最有效地减少这种偏见的干预措施。这些结果将为学术辩论提供信息,例如对STEM中女性的偏见是否随着时间的推移而减少,是否在几乎所有的培训和职业环境中持续存在,或者在不同的环境和STEM领域中以更细微的方式变化。这项工作的最终目的是帮助各组织(a)打破可能直接阻碍妇女进入STEM领域的同侪和导师歧视文化,(B)减轻可能使妇女退出STEM领域的歧视和排斥的累积经验。项目研究结果的传播将特别关注高等教育机构的可操作见解,例如男女STEM教师在教学,指导和服务活动中可以采用的减少偏见策略。该项目由EHR核心研究(ECR)计划资助,该计划支持推进STEM学习和学习环境的基础研究,扩大STEM参与和STEM劳动力发展的工作。综合将包括两组研究:(a)测试STEM领域是否存在性别偏见的研究(偏倚识别研究)和(B)评价减少此类偏倚的干预措施的研究(偏倚减少研究)。对于这两种情况,综合将侧重于随机实验设计,例如将简历上的名字从John改为Jennifer(偏见识别研究)或分配一些人接受或不接受多样性培训(偏见减少研究)。专注于实验设计最大限度地提高了证据的严谨性,因为它们有助于在测试偏倚和评估干预效果时排除潜在的混淆。所有STEM领域都将有资格接受审查,涵盖本科教育到学术和非学术劳动力,以提供对性别偏见存在的地方以及干预措施如何减少这些偏见的强大而全面的了解。与传统的文献综述相比,该团队将使用严格的系统综述和荟萃分析方法,以提高审查过程的透明度,减少审查者的偏见,并确保项目的结果是稳健的,全面的现有高质量证据。统计分析将侧重于理解特定的上下文特征(例如,正式问责制、学科领域、干预设计)可以解释在确定和减少STEM中的性别偏见方面的混合结果。这些知识可以帮助大学、公司和其他组织确定哪些地方最需要有针对性的干预,以及哪些策略对减轻偏见最有效。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

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David Miller其他文献

PlasmaCap EBA: An innovative method of isolating plasma proteins from human plasma
PlasmaCap EBA:一种从人血浆中分离血浆蛋白的创新方法
  • DOI:
    10.1111/vox.13388
  • 发表时间:
    2022
  • 期刊:
  • 影响因子:
    2.7
  • 作者:
    David Miller;G. Vanderlee;Olivier Vaute;Mark Krause
  • 通讯作者:
    Mark Krause
Idiopathic Pulmonary Fibrosis: PhysiologicTests,QuantitativeCT Indexes,andCTVisualScoresas PredictorsofMortality 1
特发性肺纤维化:生理测试、定量 CT 指数和 CT 视觉评分作为死亡率的预测因素 1
  • DOI:
  • 发表时间:
    2008
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Alan C Best;Ji;Anne M. Lynch;C. Bozic;David Miller;G. Grunwald;D. Lynch
  • 通讯作者:
    D. Lynch
Authority and Immigration
当局和移民
  • DOI:
    10.1177/00323217211046423
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    3.1
  • 作者:
    David Miller
  • 通讯作者:
    David Miller
Planet Four: Probing springtime winds on Mars by mapping the southern polar CO2 jet deposits
第四号行星:通过绘制南极二氧化碳喷射沉积物来探测火星上的春季风
  • DOI:
    10.1016/j.icarus.2018.08.018
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    3.2
  • 作者:
    K. Aye;M. Schwamb;G. Portyankina;C. Hansen;Adam McMaster;G. Miller;B. Carstensen;C. Snyder;M. Parrish;S. Lynn;C. Mai;David Miller;R. Simpson;Arfon M. Smith
  • 通讯作者:
    Arfon M. Smith
Human values as catalysts and consequences of social innovations
人类价值观作为社会创新的催化剂和后果
  • DOI:
    10.1016/j.forpol.2019.03.006
  • 发表时间:
    2019
  • 期刊:
  • 影响因子:
    4
  • 作者:
    Simo Sarkki;Andrej Ficko;David Miller;C. Barlagne;Mariana Melnykovych;Mikko Jokinen;I. Soloviy;M. Nijnik
  • 通讯作者:
    M. Nijnik

David Miller的其他文献

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{{ truncateString('David Miller', 18)}}的其他基金

Collaborative Research: SaTC: EDU: Dual-track Role-based Learning for Cybersecurity Analysts and Engineers for Effective Defense Operation with Data Analytics
协作研究:SaTC:EDU:网络安全分析师和工程师基于角色的双轨学习,通过数据分析实现有效的防御操作
  • 批准号:
    2228002
  • 财政年份:
    2023
  • 资助金额:
    $ 106.74万
  • 项目类别:
    Standard Grant
Broadening the Discovery Potential of the LHC: Instrumentation, Algorithms, and Training for Physics with the ATLAS Experiment and Direct Axion Detection
扩大大型强子对撞机的发现潜力:通过 ATLAS 实验和直接轴子探测进行物理仪器、算法和培训
  • 批准号:
    2310094
  • 财政年份:
    2023
  • 资助金额:
    $ 106.74万
  • 项目类别:
    Continuing Grant
RAPID: Understanding and Supporting K-12 School Leaders' AI-related Decision-making
RAPID:理解和支持 K-12 学校领导的人工智能相关决策
  • 批准号:
    2333764
  • 财政年份:
    2023
  • 资助金额:
    $ 106.74万
  • 项目类别:
    Standard Grant
Collaborative Research: How to get SMAL: Studying island dwarfism to find Shared Molecular mechanisms Across Life history traits
合作研究:如何获得 SMAL:研究岛屿侏儒症以寻找跨生命史特征的共享分子机制
  • 批准号:
    2222088
  • 财政年份:
    2023
  • 资助金额:
    $ 106.74万
  • 项目类别:
    Standard Grant
ECR Hub: Advancing the Long-Term Potential of Fundamental Research
ECR 中心:提升基础研究的长期潜力
  • 批准号:
    2208422
  • 财政年份:
    2022
  • 资助金额:
    $ 106.74万
  • 项目类别:
    Continuing Grant
Improving Undergraduates’ Motivation and Retention in STEM Through Classroom Interventions: A Meta-Analysis
通过课堂干预提高本科生学习 STEM 的积极性和保留率:荟萃分析
  • 批准号:
    2110368
  • 财政年份:
    2021
  • 资助金额:
    $ 106.74万
  • 项目类别:
    Standard Grant
Building for Future Discoveries: Instrumentation, Algorithms, and Training for Physics with the ATLAS Experiment
为未来的发现而构建:通过 ATLAS 实验进行物理仪器、算法和培训
  • 批准号:
    2013010
  • 财政年份:
    2020
  • 资助金额:
    $ 106.74万
  • 项目类别:
    Continuing Grant
Collaborative Research: Implementing Multi-institutional Classroom-based Undergraduate Research Experiences to Study the Impact of Environmental Changes on Salamander Populations
合作研究:实施基于多机构课堂的本科生研究经验,研究环境变化对蝾螈种群的影响
  • 批准号:
    1914791
  • 财政年份:
    2019
  • 资助金额:
    $ 106.74万
  • 项目类别:
    Standard Grant
The Development of Gender Stereotypes About STEM Abilities: A Meta-Analysis
关于 STEM 能力的性别刻板印象的发展:荟萃分析
  • 批准号:
    1920401
  • 财政年份:
    2019
  • 资助金额:
    $ 106.74万
  • 项目类别:
    Standard Grant
BOOST 2015 Workshop Hosted by the Enrico Fermi Institute at the University of Chicago; Chicago, IL; August 10-14, 2015.
BOOST 2015 研讨会由芝加哥大学恩里科费米研究所主办;
  • 批准号:
    1506139
  • 财政年份:
    2015
  • 资助金额:
    $ 106.74万
  • 项目类别:
    Standard Grant

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Reducing HIV vaccine and prevention hesitancy among sexual and gender minority adolescents
减少性少数和性别少数青少年对艾滋病毒疫苗和预防的犹豫
  • 批准号:
    10620502
  • 财政年份:
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Reducing Racial and Gender Achievement Gaps in STEM: Use of Natural Language Processing to Understand Why Affirmation Interventions Improve Performance
缩小 STEM 中的种族和性别成就差距:利用自然语言处理来理解为什么肯定干预可以提高绩效
  • 批准号:
    1420446
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    2014
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    $ 106.74万
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Reducing Violence and Victimization in Adolescence: A Sex and Gender Based Analysis and Intervention Strategy
减少青春期暴力和受害:基于性和性别的分析和干预策略
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    217682
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    2010
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    $ 106.74万
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Reducing Barriers to Gender Equity in STEM Fields by Reducing Stereotype Threats
通过减少刻板印象威胁来减少 STEM 领域性别平等的障碍
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Reducing Barriers to Gender Equity in STEM Fields by Reducing Stereotype Threats
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    7938888
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Assessing and Reducing Gender Bias in STEM Recruitment, Mentorship and Evaluation
评估和减少 STEM 招聘、指导和评估中的性别偏见
  • 批准号:
    8334029
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Assessing and Reducing Gender Bias in STEM Recruitment, Mentorship and Evaluation
评估和减少 STEM 招聘、指导和评估中的性别偏见
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    8137746
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Assessing and Reducing Gender Bias in STEM Recruitment, Mentorship and Evaluation
评估和减少 STEM 招聘、指导和评估中的性别偏见
  • 批准号:
    7713864
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    2009
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    $ 106.74万
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Understanding and Reducing the Gender Gap in Math and Science: Cognitive, Social, and Neural Mechanisms in Identity Threat
理解和缩小数学和科学领域的性别差距:身份威胁中的认知、社会和神经机制
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    0910373
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