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"Men and Masculinity at Work: Implications for Theory, Research, and Practice"

2014· article· en· W2323742627 on OpenAlexaboutno aff
Lilia M. Cortina, Verónica Caridad Rabelo

Bibliographic record

VenueAcademy of Management Proceedings · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsMasculinitySociologyHarassmentOrganizational cultureGender studiesSocial psychologyGender schema theoryPsychologyPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Although men constitute the majority of most workforces, comparatively little organizational research has examined the roles of men and masculinity within organizational culture and behavior. This session will complicate and deepen our understanding of men and masculinity in the workplace by addressing the following questions: what is the meaning and relevance of masculinity in the workplace? What is meant by a masculine organizational culture? What are some benefits—to individuals and organizations—of masculinity in the workplace? What are some potential consequences? To what extent can an understanding and application of masculinity theory help us to identify, or even challenge, inequality in the workplace? First, Alyson Byrne and Julian Barling examine marital well-being among wives who out-earn their husbands. Second, Thorsten Busch, Florence Chee, and Alison Harvey analyze gendered hostilities perpetuated via digital media as a matter of corporate social responsibility. Following the theme of institutional culture, Mandy O’Neill and Nancy Rothbard present work on a field study of fire stations addressing the intersection of masculine environments, emotions, health, and job performance. Fourth, Tim Bauerle, Alyssa McGonagle, and Vicki Magley test the relationship between occupational fatalities and gender representation. To conclude, Mikki Hebl will serve as Discussant and facilitate a lively conversation about theoretical, methodological, and practical considerations with respect to masculinity, employee behavior, and organizational culture in a variety of work contexts. In conclusion, this session presents research on masculinity to further a more nuanced and holistic research program on gender in organizations. Corporate Responsibility and the Governance of Harassment in Online Game Spaces Presenter: Thorsten Busch; Concordia U. Presenter: Florence Chee; Loyola U. Chicago Presenter: Alison Harvey; U. of Leicester Is Love All You Need? Debunking Assumptions about Masculinity and Work-Family Conflict Presenter: Olivia Amanda O'Neill; George Mason U. Presenter: Nancy Rothbard; U. of Pennsylvania When Wives Bring Home the Job Status: The Effect of Job Status Leakage on Marital Instability Presenter: Alyson Byrne; U. of Manitoba Presenter: Julian Barling; Queen's U. Mere Overrepresentation? Using Injury and Job Analysis Data to Explain Men’s Workplace Fatalities Presenter: Timothy Bauerle; U. of Connecticut Presenter: Alyssa K. McGonagle; Wayne State U. Presenter: Vicki J. Magley; U. of Connecticut

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)0.069
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.069
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0690.040
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.008
Science and technology studies0.0230.080
Scholarly communication0.0260.026
Open science0.0060.015
Research integrity0.0100.016
Insufficient payload (model declined to judge)0.0070.001

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 head0.261
GPT teacher head0.420
Teacher spread0.158 · 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 designQualitative
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

Citations0
Published2014
Admission routes1
Has abstractyes

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