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Record W1976276191 · doi:10.1080/10286630902878568

Cultural spending in Ontario, Canada: trends in public and private funding

2009· article· en· W1976276191 on OpenAlexaffabout
Barbara Jenkins

Bibliographic record

VenueInternational Journal of Cultural Policy · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsThe artsGovernment (linguistics)Public fundingCultural economicsGovernment spendingPrivate sectorCultural policyEconomicsPublic administrationEconomic growthPolitical scienceMarket economyLawWelfare

Abstract

fetched live from OpenAlex

Governments around the world have accepted the idea that spending on culture can have economic side‐effects such as attracting high technology industry, regenerating urban economies, or increasing a country’s status in the global economy. As governments increased their cultural spending, however, critics charged that their interest reflected an instrumental approach to funding the arts that prioritized large cultural organizations and iconic building projects with the potential to attract tourists over more mundane matters such as operating funding. This study examines flows of government and private funding to cultural organizations in the province of Ontario, Canada on the basis of the size of these organizations. The data show that in real terms, government operating grants to organizations that have received large infrastructural grants for building projects were lower in 2006 than in the pre‐spending cut era of 1990. When a larger group of arts organizations is examined by size, the reality is more complicated than either advocates or critics of the instrumental approach claim. One universal pattern across all size categories is the increase in private funding to cultural organizations in Ontario.

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 score0.237
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.091
GPT teacher head0.356
Teacher spread0.265 · 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

Citations12
Published2009
Admission routes2
Has abstractyes

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