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Record W4394631129 · doi:10.17118/11143/20533

Le bebelis : un médicament traditionnel féminin à usages multiples

2023· article· fr· W4394631129 on OpenAlexaff
Obrillant Damus

Bibliographic record

VenueAnthropologie des savoirs des Suds · 2023
Typearticle
Languagefr
FieldAgricultural and Biological Sciences
TopicAnimal Diversity and Health Studies
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMultipleComputer scienceMathematicsArithmetic

Abstract

fetched live from OpenAlex

Découvert par hasard lors d'un atelier de dialogue local avec des titulaires de savoirs du Sud sur des thèmes divers, le bebelis est un médicament traditionnel utilisé par des femmes rurales en Haïti pour traiter des problèmes de santé rencontrés lors de la période des menstruations, de grossesse et après l'accouchement.Les propriétés multiples de ce médicament féminin (purificatrices, réparatrices, embellificatrices, menstrogènes, etc.) en font un produit d'une importance inestimable dans les communautés rurales peu ou non biomédicalisées.Quand, comment et pourquoi de nombreuses femmes rurales l'utilisent-elles ?Quelles sont les représentations populaires du corps féminin explicatives des usages du bebelis ?L'objectif principal de cet article est de décrire, dans la perspective de l'anthropologie des médicaments traditionnels, les propriétés de ce phytomédicament Mots-clés : Bebelis, médicament traditionnel, titulaires de savoirs du Sud, problèmes de santé, femmes rurales. Bebelis: a traditional multipurpose medicine for women

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: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.037

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 studies0.0100.017
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0030.005
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 head0.186
GPT teacher head0.332
Teacher spread0.146 · 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
Published2023
Admission routes1
Has abstractyes

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