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Record W4380874384 · doi:10.3138/ctr.136.020

Edmonton Theatre Practitioners Give Voice to the Less Heard: A Report on Edmonton as Cultural Capital of Canada 2007

2008· article· en· W4380874384 on OpenAlexvenueaboutno aff
Christopher Grignard

Bibliographic record

VenueCanadian Theatre Review · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsThe artsGovernment (linguistics)Cultural capitalPopulationCapital (architecture)Performing artsSociologyPolitical sciencePublic relationsPublic administrationMedia studiesEconomic growthVisual artsSocial scienceLawArt

Abstract

fetched live from OpenAlex

The federal government created Cultural Capitals of Canada in 2002. According to its Web site, the national program was created to “recognize and support Canadian municipalities for special activities that harness the many benefits of arts and culture in community life.” Each year, Canadian municipalities compete to be the nation's Cultural Capital; there are three categories, determined by population, for which they can apply. In 2007, Edmonton was chosen as Cultural Capital in the category of population over 125,000. Since the program's inception, cities awarded Cultural Capital status in this category have included Vancouver, Regina, Toronto and Saskatoon. Being named Cultural Capital comes with the challenging task of fulfilling a long list of objectives devised in response to the program's specific criteria related to arts and culture. The city that receives the award has to create a number of events to promote and celebrate the arts and to generate funding opportunities that fulfil one of the program's primary goals: to integrate arts and culture into community planning.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.078
Threshold uncertainty score0.567

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0080.002
Scholarly communication0.0060.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.040
GPT teacher head0.290
Teacher spread0.250 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2008
Admission routes2
Has abstractyes

Explore more

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