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Record W4210694857 · doi:10.4000/gradhiva.6145

Entretien avec Nicole O’Bomsawin. Le wampum et la culture abénakise, entre passé et présent

2022· article· fr· W4210694857 on OpenAlexaffabout
Nicole O’Bomsawin, Clémence Fort, Paz Núñez‐Regueiro, Nikolaus Stolle, Leandro Varison

Bibliographic record

VenueGradhiva · 2022
Typearticle
Languagefr
FieldArts and Humanities
TopicCultural Identity and Heritage
Canadian institutionsInstitut National de la Recherche ScientifiqueMusée de la Civilisation
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Anthropologue et muséologue de formation, Nicole O’Bomsawin est originaire d’Odanak, et membre de la Première Nation des Abénakis. Entre 1986 et 2006, elle a dirigé le Musée des Abénakis, première institution muséale autochtone du Québec fondée en 1965. Aujourd’hui enseignante d’anthropologie à l’institut Kiuna et collaboratrice de l’Institut national de recherche scientifique (INRS) au Québec, elle œuvre notamment à l’établissement de nouvelles stratégies pour la promotion d’une collaboration plus étroite entre les institutions publiques et de recherche et les communautés autochtones. Elle est aussi engagée auprès de l’organisme Kapakan, qui encourage le travail avec les aînés et coordonne de nombreux évènements et rencontres intergénérationnels. Également conteuse, Nicole O’Bomsawin participe à des festivals de contes pour faire connaître l’imaginaire des Premières Nations. Son travail pionnier au musée d’Odanak et son engagement pour la préservation de la tradition orale et la transmission des savoirs autochtones en font l’une des figures de proue de la muséologie et de la culture autochtones au Québec.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.846
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.004
Scholarly communication0.0040.003
Open science0.0010.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0110.002

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 head0.017
GPT teacher head0.244
Teacher spread0.228 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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