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Record W2062362675 · doi:10.1080/10286630802281921

Can national cultural policy approaches be used for sub‐national comparisons? An analysis of the Québec and Ontario experiences in Canada

2008· article· en· W2062362675 on OpenAlexafffundabout
Monica Gattinger, Diane Saint‐Pierre

Bibliographic record

VenueInternational Journal of Cultural Policy · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversity of Ottawa
FundersArts Council EnglandInstitut national de la recherche scientifique
KeywordsCultural policyNational PolicyExploratory researchComparative researchPolitical scienceAdministration (probate law)Public administrationPolicy analysisRegional scienceSociologySocial scienceLaw

Abstract

fetched live from OpenAlex

There has been relatively little comparative research undertaken on sub‐national cultural policy. This article aims to contribute to the development of sub‐national comparative studies by assessing the utility of national cultural policy approaches for comparative research at the sub‐national level in Canada. Drawing on studies of national cultural policy, the authors develop three main approaches to cultural policy and administration – the French, British and hybrid approaches – and explore their applicability to the origin and evolution of cultural policy and administration in the Canadian provinces of Québec and Ontario. This exploratory research suggests there is room for optimism in drawing on national‐level experiences to undertake sub‐national comparative cultural policy research, particularly for comparisons over broad time periods. The study also suggests that it will be important in subsequent research to further elaborate the models for present‐day comparative analysis and to refine and adapt them to reflect specificities at the provincial level of analysis.

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 score0.242
Threshold uncertainty score0.999

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 science0.0010.000
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 head0.170
GPT teacher head0.356
Teacher spread0.186 · 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

Citations30
Published2008
Admission routes3
Has abstractyes

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