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Record W7005173290

Pluralismes juridiques et interculturalités : Regards croisés droit et anthropologie

2016· article· fr· W7005173290 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) · 2016
Typearticle
Languagefr
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic JusticePluralism (philosophy)Social justiceMiller
DOInot available

Abstract

fetched live from OpenAlex

Présentation. Les vies du pluralisme, entre l’anthropologie et le droit, Emmanuelle Piccoli, Geneviève Motard et Christoph Eberhard Pourquoi, en Afrique, «le droit » refuse-t-il toujours le pluralisme que le communautarisme induit ? , Étienne Le Roy Conjugaisons juridiques : des dynamiques du droit en situation migratoire, Dominik Kohlhagen Immigrants et réfugiés au prisme de la vie sociale des droits, Francine Saillant, Joseph J. Lévy et Alfredo Ramirez-Villagra Droit et cultures en sol français : récits de femmes juives et musulmanes à l’aube et au crépuscule du divorce, Pascale Fournier La diversité culturelle en procès : l’expérience de la justice belge par les familles à composante migratoire, quels enjeux pour le pluralisme juridique ?, Caroline Simon et Barbara Truffin Quand la coutume fait Loi. Du terrain anthropologique inuit au rôle de témoin-expert devant les instances juridiques, Bernard Saladin d’Anglure Sur la frontière : les Salish du littoral et l’érosion de la souveraineté, Bruce Granville Miller Pluralisme juridique et contemporanéité des droits et des responsabilités territoriales chez les Atikamekw Nehirowisiwok, Benoit Éthier ENTREVUE, Prendre le droit autochtone au sérieux , Entretien avec Hadley Friedland, Geneviève Motard et Mathieu-Joffre Lainé

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)0.004
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 score0.113
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0210.026
Scholarly communication0.0120.004
Open science0.0010.007
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0080.001

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 head0.012
GPT teacher head0.247
Teacher spread0.235 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

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