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Record W4393019808 · doi:10.7202/1106910ar

Pitquhivut Ilihaqtavut (« en savoir davantage sur notre culture ») : Une approche collaborative de l’archéologie et du savoir traditionnel dans l’Inuit Nunangat

2023· article· fr· W4393019808 on OpenAlexaffvenueabout
Max Friesen, Emily Angulalik, Kim Crockatt, Pamela Hakongak Gross

Bibliographic record

VenueÉtudes/Inuit/Studies · 2023
Typearticle
Languagefr
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsGovernment of NunavutUniversity of Toronto
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Cet article décrit une collaboration à long terme entre le Pitquhirnikkut Ilihautiniq/Kitikmeot Heritage Society (PI/KHS) de Cambridge Bay, au Nunavut, et l’Université de Toronto. Cette association patrimoniale de Kitikmeot, le PI/KHS, est une organisation très active, régie par des aînés, dont les activités vont de l’histoire orale au savoir traditionnel en passant par la langue, la toponymie, les programmes scolaires et la gestion d’un musée. Ses membres collaborent avec des archéologues de l’Université de Toronto depuis 1999 afin d’élargir leur programmation, d’en savoir davantage sur les périodes les plus anciennes de leur région, et ils procurent en outre des opportunités d’enregistrer le savoir traditionnel et d’impliquer les jeunes Inuit dans les programmes patrimoniaux. Nous discutons de l’histoire de cette collaboration et de ses aspects organisationnels pratiques, en concluant par des réflexions sur les raisons pour lesquelles elle continue de fonctionner au bout de plus de 20 ans.

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)0.005
metaresearch head score (Gemma)0.003
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 score0.635
Threshold uncertainty score0.725

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0180.012
Scholarly communication0.0100.004
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.128
GPT teacher head0.375
Teacher spread0.247 · 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
Published2023
Admission routes3
Has abstractyes

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