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Cycles of Sameness and Difference in LGBT Social Movements

2016· article· en· W1943240870 on OpenAlexaff
Amin Ghaziani, Verta Taylor, Amy L. Stone

Bibliographic record

VenueAnnual Review of Sociology · 2016
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLesbianScholarshipMainstreamQueerTransgenderGender studiesSocial movementSociologyFeminismIdentity (music)Political sciencePoliticsLawAesthetics

Abstract

fetched live from OpenAlex

Research on lesbian, gay, bisexual, and transgender (LGBT) movements has accelerated in recent years. We take stock of this literature with a focus on the United States. Our review adopts a historical approach, surveying findings on three protest cycles: gay liberation and lesbian feminism, queer activism, and marriage equality. Existing scholarship focuses primarily on oscillations of the movement's collective identity between emphasizing similarities to the heterosexual mainstream and celebrating differences. We contrast earlier movement cycles mobilized around difference with efforts to legalize same-sex marriage. Our review highlights the turning points that led to shifts in protest cycles, and we trace the consequences for movement outcomes. Scholarship will advance if researchers recognize the path-dependent nature of social movements and that sameness and difference are not oppositional, static, or discrete choices. We conclude by recommending directions for future research.

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)0.004
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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.016
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0010.002
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 head0.042
GPT teacher head0.422
Teacher spread0.379 · 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
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

Citations114
Published2016
Admission routes1
Has abstractyes

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