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Record W4394631139 · doi:10.17118/11143/20529

La pédagogie régénératrice et réparatrice

2023· article· fr· W4394631139 on OpenAlexaff
Obrillant Damus

Bibliographic record

VenueAnthropologie des savoirs des Suds · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsSociology

Abstract

fetched live from OpenAlex

La pédagogie régénératrice et réparatrice renvoie à un ensemble de méthodes et de savoirs visant à nous régénérer nous-mêmes, à régénérer les autres et à réparer le passé et le présent dans une perspective de durabilité humaine, écologique et planétaire.Elle vise à réduire les processus de destruction de soi, des autres humains et des non-humains.Le rôle principal de cette pédagogie alternative et transgressive est de lutter contre l'approche néolibérale de l'éducation hégémonique qui participe à la destruction des savoirs (épistémicides), des identités (identicides), des cultures (ethnocides), des ethnies (génocides), des milieux naturels (écocides) et des animaux (zoocides).Pour atteindre ces objectifs, l'éducation régénératrice et réparatrice se veut être transdisciplinaire, autrement dit elle prétend transcender les frontières entre les disciplines.La régénération et la réparation en éducation nécessitent de fabriquer des citoyens et des citoyennes capables de comprendre que le monde entier est un seul pays et que nous avons tous et toutes un destin commun, où que nous soyons.

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)0.008
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0050.026
Scholarly communication0.0110.010
Open science0.0020.009
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0160.005

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 head0.511
GPT teacher head0.561
Teacher spread0.051 · 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 designTheoretical or conceptual
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

Citations1
Published2023
Admission routes1
Has abstractyes

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