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Record W151189905 · doi:10.4000/terrain.1005

Les animaux comme partenaires de chasse

2000· article· fr· W151189905 on OpenAlexaffabout
Harvey A. Feit

Bibliographic record

VenueTerrain · 2000
Typearticle
Languagefr
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEthnologyHumanitiesReciprocity (cultural anthropology)SociologyAnthropologyArt

Abstract

fetched live from OpenAlex

Chez les chasseurs cris de la région de la baie James, dans le nord du Québec, le monde de la pensée et celui des animaux interfèrent souvent, au gré des divers événements de la vie et des activités quotidiennes – chasse, relations sociales, luttes politiques. Tout comme les Ojibwa décrits par A. I. Hallowell, les Cris ne font pas de distinction radicale entre nature et société, ou entre humains et animaux, mais vivent dans un monde animé par différentes sortes de personnes. Si les animaux sont crédités d’une pensée aux yeux des chasseurs cris, ces derniers ne sauraient cependant avoir qu’un accès indirect et incomplet à cette pensée. La chasse crée des contacts avec le monde non humain. Ces expériences nouvelles sont en adéquation profonde avec les habitudes des Cris et confirment par là même la réalité de ce monde autre. Les grandes ruptures, dans ce cosmos social, sont le résultat d’actes asociaux tels que l’exploitation des animaux et des hommes perpétrée par des « cannibales de la forêt » ou des non-Cris. Au milieu de toutes les dégradations causées à leurs terres par l’industrie, les animaux incarnent idéalement – mais aussi très physiquement – le maintien de cette relation de réciprocité qui confirme aux Cris leur propre permanence.

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 score0.427
Threshold uncertainty score0.849

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 studies0.0100.007
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0190.002

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 head0.117
GPT teacher head0.417
Teacher spread0.300 · 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

Citations1
Published2000
Admission routes2
Has abstractyes

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