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Record W2562053116 · doi:10.7202/1038039ar

Ossements animaux dans l’art autochtone actuel

2016· article· fr· W2562053116 on OpenAlexaffvenue
Gabrielle Marcoux

Bibliographic record

VenueRecherches amérindiennes au Québec · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesArtPhilosophy

Abstract

fetched live from OpenAlex

Les ossements animaux occupent des fonctions pratiques et rituelles extrêmement variées et complexes au sein des cultures ancestrales atikamekw et innue, que ce soit sur le plan spirituel, alimentaire, artistique ou autre. Encore aujourd’hui, en plus d’évoquer le trépas, les ossements font référence aux pratiques de communication avec les esprits animaux – comme la scapulomancie – et à la vision holistique du monde chez les peuples algonquiens. En intégrant des peaux et des fragments de squelettes animaux à leurs oeuvres portant sur des réalités contemporaines de pertes et de négociations intimes et collectives d’ordre culturel et territorial, les artistes Sonia Robertson et Eruoma Awashish parviennent à réactualiser le pouvoir de guérison et de résurgence traditionnellement associé aux ossements. Ainsi, la mise en scène d’os permet à ces créatrices de réécrire l’histoire de lieux chargés de sens et de réaffirmer l’importance actuelle de notions spirituelles, identitaires et culturelles autochtones ancestrales dans un contexte de réaffirmation collective et individuelle.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.016
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.136
GPT teacher head0.321
Teacher spread0.186 · 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
Published2016
Admission routes2
Has abstractyes

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