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The Motif in Canadian Landscape Painting: From the Topographical to the Decorative

2018· article· en· W4252460681 on OpenAlexaffabout
Lloyd James Bennett

Bibliographic record

VenueJournal of literature and art studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicLandscape and Cultural Studies
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsMotif (music)Landscape paintingPaintingArtVisual artsAesthetics

Abstract

fetched live from OpenAlex

In Early 20th century, Canadian landscape painting had its roots in European art, especially in impressionism and subsequent developments. When Claude Monet took to working in a series to capture changing light, he sought to simplify his subject; the painter would focus on a motif: a passage of river, a tree, or a building facade. This editing out of landscape information led to a more abstract work where one might focus on the color or brushwork in the painting. This method led to a release from the task of measuring for accuracy for the viewer. Painting became sensory and not imitative where one was free to enjoy pure painting that spoke directly to a sensation or a delight in an unsuspected arrangement. At the end of the 19th century, Art Nouveau designs were printed in international journals, like The Studio and were studied in commercial design shops, like Grip Ltd., Toronto. The nucleus of Canada's leading painting group-the Group of Seven, was formed out of commercial art and, to some extent, designs one could see reproduced in magazines. Tom Thomson took these motifs; these abstracted forms and envisioned them in the landscape of Ontario through his plein air sketches. Lawren Harris stylized trees and mountain forms into abstractions of pure delight and A. Y. Jackson set his hills in the landscapes of rural Quebec where repeating rhythms trumped topography accuracy. This paper will highlight these Canadian painters as they introduced the motif that moved landscape painting to decoration of the most satisfying kind.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
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
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
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 head0.020
GPT teacher head0.250
Teacher spread0.231 · 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 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

Citations0
Published2018
Admission routes2
Has abstractyes

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