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Record W4303431325 · doi:10.7202/1091315ar

« Vivre comme des animaux »

2022· article· fr· W4303431325 on OpenAlexvenueno aff
Pierre-Alexandre Paquet

Bibliographic record

VenueAnthropologie et Sociétés · 2022
Typearticle
Languagefr
FieldEnvironmental Science
TopicRangeland Management and Livestock Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

La foresterie sociale dans la décennie 1970, la cogestion à partir de 1990 et la reconnaissance de la constitutionnalité des droits forestiers en 2006 laissaient présager que le régime forestier de l’Inde, demeuré une prérogative étatique depuis l’ère coloniale, allait progressivement être soumis à des processus de décisions démocratiques. Toutefois, le durcissement en parallèle des lois sur la protection de la biodiversité a permis au Département des forêts de garder ses privilèges et son autorité morale sur la forêt dans plusieurs états indiens, souvent au détriment des population locales. Cet article scrute les conceptions du soi et de la citoyenneté des éleveurs nomades Van Gujjars de l’Uttar Pradesh et de l’Uttarakhand, qu’ils vivent à l’intérieur ou à proximité du parc national de Rajaji, à travers le prisme que leur offre spontanément leurs contacts quotidiens avec des animaux sauvages et domestiques : tigres et léopards, hirondelles, macaques et buffles. Considérant une gamme de rapports matériels et langagiers au sein desquels s’immiscent des figures animalières, cet article mobilise l’ethnographie multiespèce pour mettre en lumière des principes et des valeurs qui sous-tendent les revendications citoyennes des habitants traditionnels des forêts et analyser des mécanismes d’inclusion et d’exclusion propres aux zones forestières en Inde.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.027
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.237
GPT teacher head0.513
Teacher spread0.276 · 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 designNot applicable
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 routes1
Has abstractyes

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