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Record W2920079010 · doi:10.3138/jcs.2018-0003

Imagining the Exceptional Canada: Nation, Art, and Social Change in Canada’s Charitable Sector

2019· article· en· W2920079010 on OpenAlexvenueaboutno aff
Adam Saifer

Bibliographic record

VenueJournal of Canadian Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyCognitive reframingPoliticsInjusticeThe artsPolitical economyPolitical scienceMedia studiesLaw

Abstract

fetched live from OpenAlex

This article draws on a case study of the Michaëlle Jean Foundation—a Canadian arts-based charitable organization—to examine processes of national imagining in a charitable sector shaped by neo-liberal capitalism. Utilizing interviews, field notes, and organizational documents, I show how discourses of the nation intersect with the arts to reframe political struggles along culturalist lines, such that social justice optics mask an apolitical and technical model for addressing structural injustice. While foundation-funded artists can engage in creative pushback, I argue that the neo-liberalization of the sector severely limits this national (re)imagining, either shutting it down completely or reconfiguring it in line with a depoliticized framework for social change, further reaffirming dominant mythologies of Canada. With this case-focused analysis, I hope to illuminate how the censorship of resistance in the charitable sector is not always an explicit process driven by the threat of funding withdrawal. Rather, a much more insidious form of depoliticization can occur within charitable sector contexts that have institutionalized and continue to circulate dominant discourses of the nation in their imaginings of better futures—in this instance, through intersections with the arts.

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)0.001
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 score0.234
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.074
GPT teacher head0.288
Teacher spread0.214 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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