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Trans experiences in lesbian and queer space

2011· article· en· W1485794240 on OpenAlexafffundvenueabout
Catherine J. Nash

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2011
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsBrock University
FundersBrock University
KeywordsQueerLesbianTransgenderTranssexualGender studiesSpace (punctuation)SociologyComputer science

Abstract

fetched live from OpenAlex

This article explores how individuals who identify as transgendered and transsexual men experience the internal possibilities, limitations, and resistances found in spaces identified as ‘lesbian’ or as ‘queer’ in the City of Toronto. The article draws on interview data transcribing the experiences of 12 transgender and transsexual individuals in LGBTQ (lesbian, gay, bisexual, trans, and queer) spaces. These interviews empirically illustrate how fluid and unfixed gendered and sexualized practices can transform spaces and their occupants. Further, this article considers the ways spaces may be ‘queered’ and the implications of these processes on the constitution of LGBTQ spaces. The experiences of transmen in lesbian and queer spaces bring into sharp relief the complex ways that material spaces, even those arising out of resistive impulses, incorporate disciplining expectations and new opportunities. Those who research or utilize these places must be attentive to these processes, if there is to be a serious commitment to the creation of libratory, inclusive spaces.

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)0.003
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 score0.870
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0220.026
Scholarly communication0.0080.004
Open science0.0010.011
Research integrity0.0010.002
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 head0.024
GPT teacher head0.252
Teacher spread0.229 · 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

Citations89
Published2011
Admission routes4
Has abstractyes

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