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Record W2764159160 · doi:10.1177/0886109917729666

Making Queer and Trans of Color Counterpublics

2017· article· en· W2764159160 on OpenAlexaboutno aff
Matthew Chin

Bibliographic record

VenueAffilia · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Work Education and Practice
Canadian institutionsnot available
FundersHorace H. Rackham School of Graduate Studies, University of Michigan
KeywordsQueerTransgenderSociologyEthnographyGender studiesSocial workPoliticsPeople of colorInclusion (mineral)IntersectionalityPolitical scienceAnthropologyRace (biology)

Abstract

fetched live from OpenAlex

Drawing on 2 years of ethnographic fieldwork, this article examines the accessibility organizing efforts of queer and transgender of color community initiatives in Toronto, Canada. I argue that these efforts constitute a kind of counterpublic making in which queer and trans of color organizers discursively construct the marginalized populations that they seek to include. In contrast to approaches to accessibility that prioritize conventional service institutions as the locus of social transformation, this article illustrates the significance of social workers supporting the work of existing community initiatives in their drive toward an intersectional politics of inclusion.

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)0.007
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.236

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0260.020
Scholarly communication0.0090.006
Open science0.0010.013
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.087
GPT teacher head0.433
Teacher spread0.346 · 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 designNot applicable
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

Citations7
Published2017
Admission routes1
Has abstractyes

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