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Record W4297990112 · doi:10.54664/suvh5407

(Self-)Portrayals of Mixed Cultural Identities in the Works of Emily Carr and István Fujkin

2022· article· en· W4297990112 on OpenAlexaboutno aff
Krisztina Kodó

Bibliographic record

VenueVTU Review Studies in the Humanities and Social Sciences · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsCarrMohawkPaintingBridge (graph theory)ArtArt historySociologyHistoryLiteraturePhilosophyLinguistics

Abstract

fetched live from OpenAlex

The article examines the work of two artists, Emily Carr (1871 – 1945) and István Fujkin (1953), focusing on Carr’s early and mature “Indian paintings,” and Fujkin’s “Blue Owl” series, completed between 2001 and 2005. The paintings chosen from among Carr’s works are thematically linked to Klee Wyck (1940), her first fictional work describing her travels and experiences with the First Nations People in British Columbia. Though the two artists come from different cultural backgrounds, since Carr was descended from English immigrants and Fujkin is a Hungarian born in the former Yugoslavia, there are similarities in their work. Both artists depict work across time and make use of transnational imaginaries of nature and Native Canadian cultural symbols that ultimately function as a bridge between Native and western culture. Fujkin’s talent lies in his ability to “paint the music” composed and performed by Canadian Mohawk musician Robbie Robertson. Emily Carr’s paintings offer images of her visionary world that transcends cultural identities and provides an insight into nature infused with spiritual and magical elements.

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)0.004
metaresearch head score (Gemma)0.004
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 score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0070.016
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.208
GPT teacher head0.459
Teacher spread0.251 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

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