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Record W4214625236 · doi:10.3406/asdi.2020.1179

Matakan : transmission des savoirs et images de la décolonisation en milieu autochtone au Québec / Matakan : Transmission of Knowledge and Images of Decolonization in a Indigenous Community in Québec

2020· article· fr· W4214625236 on OpenAlexaboutno aff
Laurent Jérôme, Sakay Ottawa, Patrick Moar

Bibliographic record

VenueASDIWAL Revue genevoise d anthropologie et d histoire des religions · 2020
Typearticle
Languagefr
FieldArts and Humanities
TopicCultural Identity and Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Dans cet article, nous présenterons différentes stratégies déployées dans le cadre d’un partenariat de recherche pour valoriser et transmettre les savoirs et les patrimoines d’une communauté atikamekw du Québec. À travers des camps de transmission conçus comme des écoles de la forêt, des élèves atikamekw du secondaire sont amenés à se familiariser avec différentes dimensions des patrimoines atikamekw : récits de création du monde, cosmologies, cultures matérielle et immatérielle, dynamiques territoriales et toponymies, histoire de la colonisation… Nous présenterons et analyserons la forme et les retombées de ces ateliers immersifs dans le contexte actuel des politiques de décolonisation et d’affirmation des savoirs autochtones. Nous reviendrons également sur l’importance et la pertinence de différents outils utilisés pour la mise en valeur de ce patrimoine, dont la réalisation d’une pièce de théâtre et l’utilisation des nouvelles technologies.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.152

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 studies0.0090.005
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.045
GPT teacher head0.327
Teacher spread0.283 · 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 designQualitative
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

Citations5
Published2020
Admission routes1
Has abstractyes

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