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Record W4245697698 · doi:10.32920/ryerson.14662425

Other(ing) practices: critiquing the Canada Council for the Arts' Equity Office

2021· preprint· en· W4245697698 on OpenAlexaffabout
Alexandra Danielle Capistrano

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsThe artsEquity (law)MandateRepresentation (politics)Political scienceSociologyCultural diversityMedia studiesVisual artsPublic relationsLawArtPolitics

Abstract

fetched live from OpenAlex

As part of the Canada Council for the Arts' Equity Office' s mandate, support is maintained for artists of African, Asian, Middle Eastern, Latin American, and mixed-race heritage. To fulfill this duty, grant programs have been developed specifically for these artists who are identified as 'culturally diverse'. As an art world unto itself, the Canada Council is responsible not only for creating its own conventions but also for determining what kind of artists may operate within it. With differentiated grant programs for culturally diverse artists, it appears that this group occupies a marginal space within the Canada Council's art world. Drawing from the experiences of two culturally diverse artists, this paper examines issues of belonging, representation, and identity. It was found that while the artists are aware and uncomfortable with the stigma that arises from the 'culturally diverse' label, the reception of public funding takes precedence over feelings of Otherness. Key words: Canada Council for the Arts; culturally diverse; artist; equity; Othering

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)0.026
metaresearch head score (Gemma)0.031
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: none
Teacher disagreement score0.866
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0850.070
Scholarly communication0.0270.010
Open science0.0060.013
Research integrity0.0070.015
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 head0.374
GPT teacher head0.389
Teacher spread0.016 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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