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Record W4391914750 · doi:10.31235/osf.io/g6xwk

Gendered devaluation underlies faculty retention

2024· preprint· en· W4391914750 on OpenAlexaff
Katie Spoon, Joanna Mendy, María Martínez, Mirta Galešić, Daniel B. Larremore, Aaron Clauset, Lauren A. Rivera

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsDevaluationWork (physics)SalientPsychologyPolitical scienceSocial psychologySociologyBusinessLawEngineeringFinance

Abstract

fetched live from OpenAlex

Women faculty experience academia differently from men in many ways, which can lead them to consider leaving their positions. Using a large-scale survey of 10,071 current and former tenure-track and tenured faculty, representing nearly all U.S. PhD-granting institutions and 29 diverse fields of study, we show that perceived workplace climate is the most gendered aspect of faculty life, compared to stress related to research pressures, work-life balance, and departmental support. Further, analyzing 6,615 free-text responses from the same respondents reveals that devaluation, both in formal evaluations and informal interactions, is the most gendered workplace climate factor. These patterns are especially salient among women of color and tenured women. Women report that devaluation is exacerbated when institutional leadership fails to respond to devaluation, leading many to leave their jobs. Our results highlight that successful remedies must involve organizational change rather than solely individual solutions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.495
GPT teacher head0.416
Teacher spread0.080 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainIncentives
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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