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Record W2804945795 · doi:10.1111/sena.12264

Desiring Diversity: The Limits of White Settler Multiculturalism in Queer Organizations

2018· article· en· W2804945795 on OpenAlexaboutno aff
Cameron Greensmith

Bibliographic record

VenueStudies in Ethnicity and Nationalism · 2018
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsQueerMulticulturalismDiversity (politics)Gender studiesInclusion (mineral)PoliticsSociologyEthnic groupNarrativeWhite (mutation)IndigenousService providerService (business)Media studiesPolitical scienceLawAnthropologyArt

Abstract

fetched live from OpenAlex

Abstract Multiculturalism in Canada is touted as an all‐inclusive policy and practice that celebrates difference and welcomes diversity. In 2012, gays and lesbians were included in the Discover Canada document amongst various cultural, racial, and ethnic groups, marking such inclusion as foundational in Canada's imagining of itself as tolerant and accepting. Despite these narratives of multicultural diversity, people of colour and Indigenous peoples continue to experience strife, violence, and erasure. This paper looks to the ways Canadian multiculturalism is utilized by queer and trans people as part of their understandings and imaginings of queer politics. In particular, it discusses the queer service sector and the ways queer and trans service providers do diversity and multiculturalism within their work. Findings highlight the complex ways in which queer and trans service providers utilize diversity as a tactic to create further exclusion and direct attention towards wanting, needing, and desiring diversity. The paper highlights the ways diversity is desired within the institutional walls of queer service provision and draws attention to the ways the whiteness and colonialism of the organizations themselves goes unquestioned and unexamined.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.603

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.155
GPT teacher head0.433
Teacher spread0.278 · 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 designObservational
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

Citations9
Published2018
Admission routes1
Has abstractyes

Explore more

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