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Record W4382468845 · doi:10.1515/9780773550674

Spaces <i>and</i> Places <i>for</i> Art

2017· book· he· W4382468845 on OpenAlexaboutno aff
Anne Whitelaw

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2017
Typebook
Languagehe
FieldArts and Humanities
TopicCultural Heritage Management and Preservation
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyVisual artsArt historyArt

Abstract

fetched live from OpenAlex

When the Edmonton Museum of Arts opened in 1924 it was only the second art gallery in Canada west of Toronto. Spaces and Places for Art tells the story of the financial and ideological struggles that community groups and artist societies in booming frontier cities and towns faced in establishing spaces for the cultivation of artistic taste. Mapping the development of art institutions in western Canada from the founding of the Winnipeg Art Gallery in 1912 to the 1990s heyday of art museums in Manitoba, Saskatchewan, Alberta, and British Columbia, Anne Whitelaw provides a glimpse into the production, circulation, and consumption of art in Canada throughout the twentieth century. Initially dependent on paintings loaned from the National Gallery of Canada, art galleries across the western part of the country gradually built their own collections and exhibitions and formed organizations that made them less reliant on institutions and government agencies in Ottawa. Tracing the impact of major national arts initiatives such as the Massey Commission, the funding programs of the Canada Council, and the policies of the National Museums Corporation, Whitelaw sheds light on the complex relationships between western Canada and Ottawa surrounding art. Building on extensive archival research and in-depth analysis of government involvement, Spaces and Places for Art is an invaluable explanation of the roles of cultural institutions and cultural policy in the emergence of artistic practice in Canada.

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)0.000
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.835
Threshold uncertainty score0.333

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0160.016
Scholarly communication0.0140.003
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0260.002

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 head0.049
GPT teacher head0.215
Teacher spread0.166 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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