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Record W2891020941 · doi:10.1111/josi.12289

Work as a Masculinity Contest

2018· article· en· W2891020941 on OpenAlexaff
Jennifer L. Berdahl, Marianne Cooper, Peter Glick, Robert W. Livingston, Joan C. Williams

Bibliographic record

VenueJournal of Social Issues · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMasculinityCONTESTContext (archaeology)HarassmentScholarshipSociologySocial psychologyWork (physics)PsychologyGender studiesPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Abstract We propose that a key reason why the workplace gender revolution has stalled (England, 2010) is that work remains the site of masculinity contests among men. In this article, we outline a theoretical framework for thinking about work as a masculinity contest, beginning with a brief review of scholarship on masculinity and exploring how the workplace is a context in which men feel particular pressure to prove themselves as “real men.” We identify different dimensions of masculinity along which employees may compete and how the competition may differ by work context. We propose that organizations with Masculinity Contest Cultures (MCCs) represent dysfunctional organizational climates (e.g., rife with toxic leadership, bullying, harassment) associated with poor individual outcomes for men as well as women (e.g., burnout, low organizational dedication, lower well‐being). We discuss how papers in this special issue contribute insight into MCCs and end with a discussion of the contributions made by conceptualizing work as a masculinity contest, and directions for future research.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0050.021
Scholarly communication0.0100.004
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.131
GPT teacher head0.381
Teacher spread0.250 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
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

Citations436
Published2018
Admission routes1
Has abstractyes

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