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N’etions-nous pas des enseignant·e·s? Decentrer notre pratique universitaire de la recherche

2020· article· fr· W4392829780 on OpenAlexaboutno aff
Philippe Dufort, Anahi Morales Hudon

Bibliographic record

VenueRevue Possibles · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsnot available
Fundersnot available
KeywordsPhilosophyHumanities

Abstract

fetched live from OpenAlex

La pédagogie occupe, paradoxalement, trop peu de place à l'université.Du moins, comparativement à la recherche.Comme le soutient bell hooks, « la plupart d'entre nous ne sont pas enclins à considérer la discussion sur la pédagogie comme essentielle pour notre travail académique et notre croissance intellectuelle, ou la pratique de l'enseignement comme un travail qui améliore et enrichit l'érudition » (1994, 205 1 ).Ce constat n'est pas surprenant considérant les effets de décennies de néolibéralisation et de corporatisation de l'université.bell hooks nous invite à réfléchir sur notre pratique quotidienne en posant une question toute simple : « Comment pouvons-nous servir?» (2003, 83).Pour décentrer notre pratique de la recherche, nous croyons important de la recentrer sur un service à la communauté significatif et la valorisation de la pédagogie.Ailleurs, nous abordons le service à la communauté comme une des voies possibles (Dufort et Morales Hudon 2020).Ici, nous abordons cette réflexion sous l'angle de la pédagogie et de la pratique quotidienne du professeur•e qui réussit à devenir partie prenante d'une communauté apprenante engagée 2 . 1 Toutes les citations extraites d'un livre ou d'un article en anglais ont été traduites par nos soins. 2 Cet essai se construit sur des expériences et des réflexions collectives qui impliquent nos collègues de l'École d'innovation sociale Élisabeth-Bruyère de l'Université Saint-Paul à Ottawa

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)0.050
metaresearch head score (Gemma)0.101
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: Other · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.263

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0500.101
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0150.040
Scholarly communication0.0330.036
Open science0.0030.010
Research integrity0.0120.027
Insufficient payload (model declined to judge)0.0190.006

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 head0.553
GPT teacher head0.470
Teacher spread0.083 · 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
GenreOther

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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