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Record W4382933094

Enseigner et apprendre à l'ère de l'intelligence artificielle

2023· preprint· fr· W4382933094 on OpenAlexaff
Margarida Roméro, Laurent Heiser, Alexandre Lepage, Anne Gagnebien, Audrey Bonjour, Aurélie Lagarrigue, Axel Palaude, C. Boulord, Charles-Antoine Gagneur, Chloé Mercier, Christelle Caucheteux, Dominique Guidoni-Stoltz, Florence Tressols, Julie Henry, Frédéric Alexandre, Jean-François Céci, Jérémy Camponovo, Laurent Fouché, Jean-François Métral, Lianne-Blue Hodgkins, Marie-Hélène Comte, Michel Durampart, Patricia Corieri, Paul Olry, Pauline Reboul, Philippe Bonfils, Sami Ben Amor, Simon Collin, Solange Ciavaldini-Cartaut, Thierry Viéville, Victoire Batifol, Yann‐Aël Le Borgne

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2023
Typepreprint
Languagefr
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsUniversité du Québec à MontréalUniversité de MontréalUniversité Laval
Fundersnot available
KeywordsAcculturationMultidisciplinary approachPerspective (graphical)Mathematics educationSociologyMediationComputer sciencePedagogyKnowledge managementPsychologyArtificial intelligenceSocial science
DOInot available

Abstract

fetched live from OpenAlex

International audience

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)0.005
metaresearch head score (Gemma)0.017
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 score0.022
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0100.008
Open science0.0020.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0220.010

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 head0.035
GPT teacher head0.273
Teacher spread0.238 · 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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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