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Record W3049247230 · doi:10.7202/1070475ar

Prédire ou travestir ?

2020· article· fr· W3049247230 on OpenAlexvenueaboutno aff
Aïko Cappe, Frédéric Laugrand

Bibliographic record

VenueEthnologies · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

À partir d’une ethnographie du Jour de la marmotte à Wiarton (Canada), nous analysons en quoi la marmotte révèle un profond anthropocentrisme. Dans cette ville, comme ailleurs, la marmotte ne prédit rien mais les participants travestissent ses gestes et simulent des prédictions sur une scène de théâtre. En contexte naturaliste, les humains continuent d’échouer à nouer des relations étroites avec cet animal dont ils ont fait un objet-spectacle, un véritable simulacre. La marmotte est instrumentalisée à des fins socioéconomiques si bien que la question du bien-être de l’animal est esquivée. Les pratiques divinatoires autour de la marmotte montrent qu’au sein même des sociétés modernes, cette activité se marie bien avec le spectaculaire. Les humains ne font plus danser la marmotte mais ils continuent de l’exhiber, prétendant qu’elle leur indique l’avenir. Ils ne la voient plus comme un animal nuisible mais comme une source de revenu.

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)0.003
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.398
Threshold uncertainty score0.792

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.017
Scholarly communication0.0080.005
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.137
GPT teacher head0.311
Teacher spread0.174 · 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
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

Citations1
Published2020
Admission routes2
Has abstractyes

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