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Record W310542538 · doi:10.5860/choice.49-1876

Picturing the land: narrating territories in Canadian landscape art, 1500-1950

2011· article· en· W310542538 on OpenAlexaboutno aff

Bibliographic record

VenueChoice Reviews Online · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicLandscape and Cultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsExpansiveLandscape historyCultural landscapeLandscape paintingPoliticsRepresentation (politics)PaintingDiversity (politics)Landscape archaeologyPerspective (graphical)Relation (database)AestheticsHistoryGeographySociologyLandscape designArtArchaeologyVisual artsArt historyAnthropologyPolitical scienceLawEnvironmental resource management

Abstract

fetched live from OpenAlex

The vast Canadian landscape has captured the imagination of visual artists since the first European contact. Although artistic engagement with the landscape has a long history, some periods have drawn considerable critical attention, while others have been left almost unexamined. Picturing the Land surveys work from coast to coast, from the earliest maps to postwar painting in English and French Canada, to provide a comprehensive view of Canadian landscape art. Emphasizing the ways in which social, economic, and political conditions determine representation, Marylin McKay moves beyond canonical images and traditional nationalistic interpretations by analyzing Canadian landscape art in relation to different concepts of territory. Taking an expansive and inclusive perspective on Canadian landscape art, McKay depicts this tradition in all its diversity and draws it into the larger body of Western landscape art, broadening the horizon of future study, appreciation, and criticism -- p. [4] of cover.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0220.012
Scholarly communication0.0060.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.078
GPT teacher head0.260
Teacher spread0.182 · 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
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

Citations11
Published2011
Admission routes1
Has abstractyes

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Same venueChoice Reviews OnlineSame topicLandscape and Cultural StudiesFrench-language works237,207