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Record W4313136604 · doi:10.33921/uwbf7249

The Effect of Feminist Identification on the Perceived Authenticity of Male Allies

2022· article· en· W4313136604 on OpenAlexvenueno aff
Nathalia G. Lee, Priscilla Lok‐chee Shum, Stephen C. Wright, McKenzie Bahrami

Bibliographic record

VenueJournal of Interpersonal Relations Intergroup Relations and Identity · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsEgalitarianismIdentification (biology)PerceptionSocial psychologyPsychologyRepresentation (politics)FeminismGender studiesClubSociologyPolitical sciencePoliticsMedicineLaw

Abstract

fetched live from OpenAlex

Male allies are often described as essential to reducing gender inequity. However, some men may become allies through benevolent sexist beliefs. While some women recognize this as a suboptimal form of allyship, others may interpret it as authentic. We investigated whether the type of allyship (egalitarian vs. benevolent sexist) influences women’s perceptions of authenticity and whether women’s feminist identification moderates this effect. Women (N = 132) undergraduate students completed a pre-screening survey including a measure of feminist identification. They read about and rated the authenticity of a male ally who exhibited egalitarianism or benevolent sexism in his effort to increase women representation in a male-dominated club. Women perceived the egalitarian ally as highly authentic regardless of their feminist identification. However, higher feminist identification was associated with higher perceived authenticity for the benevolent sexist ally. Results are discussed in terms of how feminist identification can influence women’s perceptions of men who help.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.322
Teacher spread0.308 · 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 teacher head, not a consensus.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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