Dataset Information

A threat in the network: STEM women in less powerful network positions avoid integrating stereotypically feminine peers.

ABSTRACT: Integrating social identity threat and structural hole theories, this work examines how social network positions affect group-based identity threats. For individuals less well positioned to bridge (or "broker") relations between unconnected friends, stigma-by-association concerns may constrain affiliation with stereotypic targets. Three experiments (Ns = 280, 232, 553) test whether women (vs. men) in male-dominated STEM (vs. female-dominated) majors avoid befriending a female target with feminine-stereotypic (vs. STEM-stereotypic) interests. Only STEM women with less brokerage (i.e., less ability to manage introductions to unconnected friends) in their existing friendship networks avoided befriending (pilot experiment) and socially integrating (Experiments 1 and 2) feminine- (vs. STEM-) stereotypic targets, despite standardized target similarity and competence. STEM women in particular anticipated steeper reputational penalties for befriending stereotypically feminine peers (Experiment 2). Social identity threat may lead women in STEM-especially those lacking brokerage-to exclude stereotypically feminine women from social networks, reinforcing stereotypes of women and STEM fields.

SUBMITTER: Bergsieker HB

PROVIDER: S-EPMC8054113 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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A threat in the network: STEM women in less powerful network positions avoid integrating stereotypically feminine peers.

Bergsieker Hilary B HB Wilmot Matthew O MO Cyr Emily N EN Grey Charnel B CB

Group processes & intergroup relations : GPIR 20200103 3

Integrating social identity threat and structural hole theories, this work examines how social network positions affect group-based identity threats. For individuals less well positioned to bridge (or "broker") relations between unconnected friends, stigma-by-association concerns may constrain affiliation with stereotypic targets. Three experiments (<i>N</i>s = 280, 232, 553) test whether women (vs. men) in male-dominated STEM (vs. female-dominated) majors avoid befriending a female target with ...[more]

PMID: 33958955

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Project description:Heterogeneity in different genomic studies compromises the performance of machine learning models in cross-study phenotype predictions. Overcoming heterogeneity when incorporating different studies in terms of phenotype prediction is a challenging and critical step for developing machine learning algorithms with reproducible prediction performance on independent datasets. We investigated the best approaches to integrate different studies of the same type of omics data under a variety of different heterogeneities. We developed a comprehensive workflow to simulate a variety of different types of heterogeneity and evaluate the performances of different integration methods together with batch normalization by using ComBat. We also demonstrated the results through realistic applications on six colorectal cancer (CRC) metagenomic studies and six tuberculosis (TB) gene expression studies, respectively. We showed that heterogeneity in different genomic studies can markedly negatively impact the machine learning classifier's reproducibility. ComBat normalization improved the prediction performance of machine learning classifier when heterogeneous populations are present, and could successfully remove batch effects within the same population. We also showed that the machine learning classifier's prediction accuracy can be markedly decreased as the underlying disease model became more different in training and test populations. Comparing different merging and integration methods, we found that merging and integration methods can outperform each other in different scenarios. In the realistic applications, we observed that the prediction accuracy improved when applying ComBat normalization with merging or integration methods in both CRC and TB studies. We illustrated that batch normalization is essential for mitigating both population differences of different studies and batch effects. We also showed that both merging strategy and integration methods can achieve good performances when combined with batch normalization. In addition, we explored the potential of boosting phenotype prediction performance by rank aggregation methods and showed that rank aggregation methods had similar performance as other ensemble learning approaches.

Dataset Information

A threat in the network: STEM women in less powerful network positions avoid integrating stereotypically feminine peers.

Publications

A threat in the network: STEM women in less powerful network positions avoid integrating stereotypically feminine peers.

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