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Record W3120713531 · doi:10.3138/jcs-2018-0032

We Have Always Been Here: Pelau MasQUEERade Disturbing Toronto Pride History

2020· article· en· W3120713531 on OpenAlexvenueaboutno aff
Raymond Lord

Bibliographic record

VenueJournal of Canadian Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLatin American and Latino Studies
Canadian institutionsnot available
Fundersnot available
KeywordsQueerPrideParadeGender studiesLesbianHuman sexualitySociologyDiasporaNarrativeTransgenderQueer theoryCitizenshipMedia studiesPoliticsHistoryPolitical scienceLiteratureArtLawArt history

Abstract

fetched live from OpenAlex

This article addresses the uneasy tensions and overlaps between race, sexuality, and citizenship, by focusing on the moments when queer diasporic people of colour slip “in” and “out” of Pride Toronto—the organization that hosts the annual Toronto lesbian, gay, bisexual, transgender, and queer Pride Parade. Drawing on Pride Toronto’s thirtieth anniversary promotional material, released in 2010, and the organization’s 2002 five-year strategic direction plan, the author applies a queer feminist diaspora reading practice to problematize how the documents, taken together, presents Pride’s queer history in Canada, which continues to be narrated by the organization in ways that de-racialize queer subjects and invoke white gays and lesbians as central to its story. Pelau MasQUEERade, a Caribbean queer diasporic group that participates in the Pride Parade, reveals how the histories of queer diasporic people of colour are strategically appropriated and belatedly visible only in sporadic moments in the documents. Pelau MasQUEERade’s presence works to disturb normative accounts of Pride, while simultaneously opening the door to overlapping histories of queer diasporic people in the event. The group reworks Pride Toronto’s de-racialized narrative of sexuality by gesturing to the ways in which race, sexuality, and notions of citizenship operate in multiple and contradictory ways.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.956
Threshold uncertainty score0.782

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.072
GPT teacher head0.300
Teacher spread0.228 · 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 teacher head, 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

Citations1
Published2020
Admission routes2
Has abstractyes

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