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Record W2897818029 · doi:10.7202/1051429ar

Genre, diversité et territoire : l’utilisation des approches narratives dans une recherche partenariale transnationale

2018· article· fr· W2897818029 on OpenAlexaffvenueabout
Chantal Doré, Jacques Caillouette, Michèle Vatz Laaroussi, Liliana Kremer, Carlos Yáñez Canal, Linamar Campos Flores

Bibliographic record

VenueRecherches sociographiques · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Humanities
Canadian institutionsUniversité de MontréalUniversité de Sherbrooke
Fundersnot available
KeywordsHumanitiesPolitical scienceSociologyArt

Abstract

fetched live from OpenAlex

Cet article présente les objectifs, la méthodologie et les résultats d’une recherche transnationale entre le Québec, l’Argentine et la Colombie menée sur deux ans (2014-2016) et portant sur les rapports sociaux de genre, la diversité et la construction démocratique des territoires. En contribution au courant d’innovation méthodologique et théorique dans le champ des pratiques de recherche partenariale, l’exposé se penche sur la fonction performative qui a caractérisé cette recherche. En libérant des espaces de narration, de dialogues publics et de mise en lien entre le proche et le lointain, ce type de recherche ouvre de nouvelles perspectives de compréhension et d’actualisation des acteurs.

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)0.021
metaresearch head score (Gemma)0.028
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 score0.073
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0060.012
Scholarly communication0.0090.007
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.304
GPT teacher head0.393
Teacher spread0.089 · 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

Citations4
Published2018
Admission routes3
Has abstractyes

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