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Artists in rural locales: market access, landscape appeal and economic exigency

2001· article· en· W2093437945 on OpenAlexafffundvenueabout
Trudi E. Bunting, Clare Mitchell

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsUniversity of Waterloo
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCensusThe artsAppealCraftGeographyRural areaScale (ratio)HandicraftEconomic growthRegional scienceSocioeconomicsSociologyPolitical scienceVisual artsArchaeologyCartographyDemographyPopulationArtEconomics

Abstract

fetched live from OpenAlex

The period 1971 to 1991 saw a significant increase in the proportion of Canadians employed in the ‘arts’. While still concentrated to a large extent in urban Canada, artists do seek out rural locations to pursue their craft. This paper identifies, interprets and classifies communities in rural Canada that specialize in the production of visual, performing and literary art. Location quotients are calculated from a custom‐tabulated run of 1991 census data on employment in the arts in all Canadian census subdivisions. We propose several factors that may account for high concentrations of artists in some rural places. Cluster analysis is used to develop a classification of Canadian rural arts communities. We identify 371 small arts centres in Canada, ranging from Cape Dorset in the Northwest Territories to Elora in southwestern Ontario. Market access, landscape appeal and economic exigency are among the location determinants isolated. Further analysis reveals that five types of arts communities exist in rural Canada. Future research on a localized scale is now necessary to uncover specific factors responsible for the prevalence of artists in the rural ecumene.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.455
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.232
Teacher spread0.217 · 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.

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

Citations32
Published2001
Admission routes4
Has abstractyes

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