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Record W2168509757 · doi:10.14288/acme.v12i2.960

Queering neighbourhoods: Politics and practice in Toronto

2015· article· en· W2168509757 on OpenAlexaboutno aff
Catherine J. Nash

Bibliographic record

VenueOpen Collections · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsQueerGender studiesPoliticsLesbianSociologyDowntownPolitical scienceGeography

Abstract

fetched live from OpenAlex

Changing political, social and economic circumstances operating across a variety of scales are transforming the socio-spatial landscapes for Lesbian, Gay, Bi, Trans and Queer (LGBTQ) people in Toronto. While the established gay village continues to be the imagined and material centre of political and social life for the LGBTQ community, various groups are increasingly utilizing other locations in the downtown core but outside the Village, particularly an area know colloquially as ‘Queer West.’ This paper argues that for some queer women/gender queers individuals, the Village is not viewed as a desirable location for social or political organising given perceptions the area is dominated by largely white, middle class, gay men. Further, the possibilities, potentials and limitations for queer women/genderqueer individuals to take up alternative locations are constituted through complex social relations and include notions of what ‘queered’ and ‘queering’ space entails and participants’ own imagined sense of place and reflecting aspects of their own classed, racialized and gendered positioning

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)0.003
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.564

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0270.012
Scholarly communication0.0060.002
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.000

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 head0.046
GPT teacher head0.365
Teacher spread0.319 · 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

Citations33
Published2015
Admission routes1
Has abstractyes

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