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Record W4379879609 · doi:10.4000/rsa.5745

Décoloniser les pédagogies universitaires au Canada et au Sénégal

2022· article· fr· W4379879609 on OpenAlexaboutno aff
Nathalie Mondain, Jean Alain Goudiaby

Bibliographic record

VenueRecherches sociologiques et anthropologiques · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Le mouvement de décolonisation au sein des institutions universitaires du continent africain s’est traduit au Sénégal par le recrutement d’un personnel africain et par un effort constant dans la confection des curricula. Qu’en est-il ailleurs, au sein d’institutions qui sont ancrées dans le passé colonial des États qui les abritent ? Le cas canadien, du fait de son histoire avec les peuples autochtones, permet aussi de mettre en perspective les dimensions oppressives du point de vue épistémologique et hiérarchique au sein des univers académiques. Cette contribution propose une réflexion sur ces enjeux à partir d’une collaboration pédagogique entre deux collègues appartenant respectivement à une institution sénégalaise et canadienne. Nous arguons que les rapports de pouvoir ancrés dans l’histoire coloniale au sein de l’université peuvent être renégociés grâce à l’ouverture d’espaces d’enseignement alternatifs. Ces derniers, en initiant des programmes et cours innovants reposant sur une collaboration décomplexée et mobilisant une pédagogie sensible peuvent permettre une réelle décolonisation pédagogique et des méthodologies de recherche.

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)0.004
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.824

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.006
Science and technology studies0.0250.016
Scholarly communication0.0090.004
Open science0.0020.008
Research integrity0.0020.004
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 head0.451
GPT teacher head0.512
Teacher spread0.062 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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