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Record W2162510385 · doi:10.1177/1097184x14549998

You Gotta Kick Ass a Little Harder Than That

2014· article· en· W2162510385 on OpenAlexaff
Matthew S. Johnston, Jennifer M. Kilty

Bibliographic record

VenueMen and Masculinities · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Security, and Conflict
Canadian institutionsUniversity of OttawaUniversity of Victoria
Fundersnot available
KeywordsPrivilege (computing)Gender studiesMasculinityHegemonic masculinityQualitative researchDominance (genetics)QueerHegemonySociologyIdentity (music)PsychologyPolitical sciencePoliticsLawSocial science

Abstract

fetched live from OpenAlex

This qualitative research explores the complex and dynamic ways in which eight hospital security men engage in hegemonic masculine practices that subordinate the gender identities of security women and marginalized men. These intensive, in-depth interviews reveal that alpha male status is accomplished through routine demonstrations of physicality and dominance over mental health patients and subordinated guards who present a feminine or queer gender identity. Security officers who resist the established codes of masculine conduct are excluded from social circles, and culturally devalued by their hypermasculine peers and superiors. Overall, this research calls for the revision of hospital security recruitment and training initiatives that privilege military background and skills, and invites scholars to give voice to the gendered voices of security women, gay men, and nursing staff.

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)0.007
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0090.006
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0160.003

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 head0.040
GPT teacher head0.280
Teacher spread0.240 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

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