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Record W2808066407 · doi:10.1111/cico.12298

Performative Progressiveness: Accounting for New Forms of Inequality in the Gayborhood

2018· article· en· W2808066407 on OpenAlexaff
Adriana Brodyn, Amin Ghaziani

Bibliographic record

VenueCity and Community · 2018
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPerformative utteranceAffect (linguistics)Prejudice (legal term)PoliticsPrivilege (computing)SociologyVisionLesbianInequalityPlacemakingSocial psychologyPower (physics)Gender studiesPolitical scienceAestheticsPsychologyLawGeographyUrban design

Abstract

fetched live from OpenAlex

Attitudes toward homosexuality have liberalized considerably, but these positive public opinions conceal the persistence of prejudice at an interpersonal level. We use interviews with heterosexual residents of Chicago gayborhoods—urban districts that offer ample opportunities for contact and thus precisely the setting in which we would least expect bias to appear—to analyze this new form of inequality. Our findings show four strategies that liberal–minded straights use to manage the dilemmas they experience when they encounter their gay and lesbian neighbors on the streets: spatial entitlements, rhetorical moves, political absolution, and affect. Each expression captures the empirical variability of performative progressiveness , a concept that describes the co–occurrence of progressive attitudes alongside homonegative actions. Our analyses have implications more broadly for how conflicting visions of diversity affect placemaking efforts; how residents with power and privilege redefine cultural enclaves in the city; and the mechanisms that undermine equality in a climate of increasing acceptance.

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)0.005
metaresearch head score (Gemma)0.018
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.129
GPT teacher head0.430
Teacher spread0.301 · 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

Citations67
Published2018
Admission routes1
Has abstractyes

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