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Record W1978773411 · doi:10.1177/0891243214542430

Doing Intersectionality

2014· article· en· W1978773411 on OpenAlexaboutno aff
Éléonore Lépinard

Bibliographic record

VenueGender & Society · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsnot available
Fundersnot available
KeywordsIntersectionalitySociologyOppressionGender studiesTypologyContext (archaeology)Identity (music)SituatedPoliticsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Intersectionality has been adopted as the preferred term to refer to and to analyze multiple axes of oppression in feminist theory. However, less research examines if this term, and the political analyses it carries, has been adopted by women’s rights organizations in various contexts and to what effect. Drawing on interviews with activists working in a variety of women’s rights organizations in France and Canada, I show that intersectionality is only one of the repertoires that a women’s rights organization might use to analyze the social experience and the political interests of women situated at the intersection of several axes of domination. I propose a typology of four repertoires that activists use to reflect on intersectionality and inclusiveness. Drawing on a quantitative and qualitative analysis of the interview data, I show that hegemonic repertoires about racial or religious identity in one national context shape the way activists and organizations understand intersectionality and its challenges. The identity of organizations, as well as their main function (advocacy or providing service), also shape their understanding of intersectional issues.

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)0.042
metaresearch head score (Gemma)0.043
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: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.043
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.004
Science and technology studies0.0210.067
Scholarly communication0.0180.039
Open science0.0040.040
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0210.004

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 head0.051
GPT teacher head0.341
Teacher spread0.290 · 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
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

Citations57
Published2014
Admission routes1
Has abstractyes

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