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Men, Masculinity, and Manhood Acts

2009· article· en· W2153787751 on OpenAlexfundno aff
Douglas Schrock, Michael Schwalbe

Bibliographic record

VenueAnnual Review of Sociology · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsnot available
FundersDalhousie University
KeywordsMasculinityDeferenceConstruct (python library)Gender studiesStrengths and weaknessesTraitPoliticsSociologyInequalitySocial psychologyPsychologyPolitical science

Abstract

fetched live from OpenAlex

In the 1980s research on men shifted from studying the “male sex role” and masculinity as a singular trait to studying how men enact diverse masculinities. This research has examined men's behavior as gendered beings in many contexts, from intimate relationships to the workplace to global politics. We consider the strengths and weaknesses of the multiple masculinities approach, proposing that further insights into the social construction of gender and the dynamics of male domination can be gained by focusing analytic attention on manhood acts and how they elicit deference from others. We interpret the literature in terms of what it tells us about how males learn to perform manhood acts, about how and why such acts vary, and about how manhood acts reproduce gender inequality. We end with suggestions for further research on the practices and processes through which males construct the category “men” and themselves as its members.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.023
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.001
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 head0.025
GPT teacher head0.361
Teacher spread0.336 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations723
Published2009
Admission routes1
Has abstractyes

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