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Record W2730023249 · doi:10.25071/2564-4033.40213

Narratives of resistance: (Re) Telling the story of the HIV/AIDS movement – Because the lives and legacies of Black, Indigenous, and People of Colour communities depend on it

2017· article· en· W2730023249 on OpenAlexafffund
Ciann Wilson, Sarah Flicker, Jean‐Paul Restoule, Ellis Furman

Bibliographic record

VenueHealth Tomorrow Interdisciplinarity and Internationality · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican Sexualities and LGBTQ+ Issues
Canadian institutionsWilfrid Laurier UniversityYork UniversityUniversity of Toronto
FundersOffice of International Science and EngineeringCenters for Disease Control and PreventionUniversity of TorontoWilfrid Laurier University
KeywordsIndigenousNarrativeGender studiesWhite (mutation)Resistance (ecology)SovereigntyPoliticsHuman immunodeficiency virus (HIV)SociologySocial movementPolitical scienceMedicineArtLawEcologyLiterature

Abstract

fetched live from OpenAlex

Centering the narratives of the intersectional struggles within the HIV movement for Indigenous sovereignty, Black and People of Colour liberation, and LGBTQ rights tirelessly fought for by Black, Indigenous, and People of Colour communities legitimates their lives and legacies within the movement; and the relevance of a focused response to the HIV epidemic that continues to wreak devastation in these communities. The recent political push for a post-HIV era solely centers the realities of middle-class white, gay men and has genocidal implications for Black, Indigenous, and People of Colour communities.Keywords: HIV/AIDS; Black, Indigenous and People of Colour communities; Social Movements; Narrative

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)0.005
metaresearch head score (Gemma)0.007
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0170.037
Scholarly communication0.0090.009
Open science0.0010.010
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0050.000

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 head0.059
GPT teacher head0.376
Teacher spread0.317 · 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

Citations8
Published2017
Admission routes2
Has abstractyes

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