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Record W4395059685 · doi:10.1515/9782763744650-002

Présentation

2020· book-chapter· fr· W4395059685 on OpenAlexaboutno aff
Daniel Clément

Bibliographic record

Venuenot available
Typebook-chapter
Languagefr
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Présentation« Les Récits de notre terre » constituent la première série de la collection « Tradition orale ».Cette série sera consacrée à l'Amérique en commençant par les Premiers Peuples qui se sont développés sur un territoire qui correspond à une partie du nord-est de l'Amérique du Nord, soit le Québec dans son ensemble ainsi que des portions de certaines provinces canadiennes et d'États américains voisins.Les Cris, auxquels ce recueil est consacré, forment démographiquement la seconde population en importance après celle des Mohawks.Avec plus de 18 000 membres inscrits, les Cris, ou Eeyou dans la langue vernaculaire, habitent un vaste territoire, l'Eeyou Istchee, qui s'étend d'est en ouest, des confins de la région saguenéenne jusqu'à la baie James et le sud de la baie d'Hudson.Les neuf communautés les plus connues sont, le long des rives de la baie James, Waskaganish, Eastmain, Wemindji et Chisasibi ; plus au nord, dans la baie d'Hudson, Whapmagoostui ; et à l'intérieur des terres, Nemaska, Waswanipi, Mistissini et Oujé-Bougoumou.La Nation crie reconnaît depuis peu une dixième communauté, Washaw Sibi, ainsi qu'une Première Nation de l'Ontario affiliée, Mocreebec.Les Cris, comme les Innus, les Atikamekw, les Algonquins, les Naskapis et d'autres appartiennent à la grande famille culturelle et linguistique algonquienne.Le cri est la langue première,

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.298
Threshold uncertainty score0.425

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0070.004
Open science0.0020.004
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.7020.429

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 head0.062
GPT teacher head0.277
Teacher spread0.216 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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