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Record W4211045865 · doi:10.4000/caliban.6040

Through the Leaves to the Trunk: Emily Carr's Invention of Nature

2019· article· fr· W4211045865 on OpenAlexaboutno aff
Biancamaria Rizzardi

Bibliographic record

VenueCaliban · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtArt history

Abstract

fetched live from OpenAlex

Emily Carr (1871–1945), a été l'une des premières artistes d'importance nationale à émerger de la Côte Ouest. Avec le Groupe des Sept, elle est devenue une figure majeure de l'art moderne canadien du vingtième siècle. Elle passa une grande partie de sa vie à Victoria où elle vivait et où elle travaillait, et où elle dut se battre pour être acceptée par la critique. Elle est surtout connue pour son œuvre qui recense les totems des peuples des Premières Nations de la province de Colombie Britannique, et pour ses paysages de forêts, peints à l'huile et décrits dans Klee Wyck (1941, "The Laughing Woman" ["La femme qui rit"], nom que les autochtones de la côte ouest lui donnent en tant que jeune femme intrépide. Dans mon article, en lisant certains de ses tableaux à la lumière de ses écrits, j'essaierai de démontrer comment, derrière les détails microscopiques, dignes d'une naturaliste amateur, ses œuvres sont dominées par des traits d’esprit rococo sublimes, par des forces inconnues, des provocations audacieuses et de soudains traits d'intuition, qui révèlent, au-delà de la "peinture de genre", le fonctionnement d'un esprit vif, éveillé, toujours en activité, et un désir de connaissance qui, à travers sa perception de la forêt, devient plus métaphysique que physique.

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 score0.966
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.010
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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