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Record W1950661058 · doi:10.1111/1468-2427.12209

‘Gay Enclaves Face Prospect of Being PassÉ': How Assimilation Affects the Spatial Expressions of Sexuality in the United States

2015· article· en· W1950661058 on OpenAlexaff
Amin Ghaziani

Bibliographic record

VenueInternational Journal of Urban and Regional Research · 2015
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMainstreamCultural assimilationHuman sexualitySexual orientationSociologyPoliticsFace (sociological concept)Assimilation (phonology)Gender studiesCentralityPerspective (graphical)Social psychologyPolitical sciencePsychologySocial scienceLaw

Abstract

fetched live from OpenAlex

Abstract Journalists, activists and academics alike predict that gay neighborhoods in the United States will disappear, yet many of their claims are unsubstantiated or overly determined by economic factors. This article examines 40 years of media accounts to identify the mechanisms that explain why these urban areas are changing. I begin with the observation that the rate of assimilation of sexual minorities into mainstream society has accelerated in today's so‐called ‘post‐gay' era. Assimilation expands the residential imagination of gays and lesbians beyond the boundaries of a specific neighborhood to the entire city itself. Furthermore, as sexual orientation recedes in centrality in everyday life, residents opine that few care if a person self‐identifies as gay or straight. These two respective mechanisms of expansion and cultural sameness bring existing economic wisdom into dialogue with a cultural and political perspective about how our shifting understandings of sexuality also affect the decisions we make about where to live and socialize.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.003
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.242
GPT teacher head0.499
Teacher spread0.257 · 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 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

Citations31
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Urban and Regional ResearchSame topicLGBTQ Health, Identity, and PolicyFrench-language works237,207