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Record W4380263877 · doi:10.1177/0095327x231178522

Masculine Conformity and Social Dominance’s Relation With Organizational Culture Change

2023· article· en· W4380263877 on OpenAlexaffabout
Michelle Enxi Deng, Adelheid A. M. Nicol, Cindy Suurd Ralph

Bibliographic record

VenueArmed Forces & Society · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsConformityDominance (genetics)Social psychologyNormativeSocial dominance orientationMasculinityPsychologyNormative social influenceInstitutionSociologySocial groupSocial changePolitical scienceGender studiesDemocracyPoliticsSocial scienceLaw

Abstract

fetched live from OpenAlex

While the workforce is becoming increasingly more modernized and diverse, masculine norms are prevalent among certain organizations that remain male-dominated. Namely, the military is an institution that promotes masculine stereotypes and a culture where such stereotypes form a normative system of hierarchy. This study, surveying 145 military cadets at the Royal Military College of Canada (RMC), found that social dominance orientation, or preference for in-group superiority and out-group inequality, was associated with higher conformity to masculine norms. Moreover, higher levels of social dominance explained the relationship between masculine conformity and less acceptance toward cultural reforms in the Canadian Armed Forces (CAF). These findings suggest that achieving true organizational culture change in the military involves challenging not only masculine norms but, more importantly, the dominant and nonegalitarian attitudes of social dominance.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.065
GPT teacher head0.277
Teacher spread0.212 · 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 designObservational
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

Citations19
Published2023
Admission routes2
Has abstractyes

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