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Record W3092443300 · doi:10.18162/ritpu-2020-v17n1-03

Qu’est-ce que le Cadre de référence de la compétence numérique?

2020· article· fr· W3092443300 on OpenAlexaffvenueabout
Thierry Karsenti, Bruno Poëllhuber, Simon Jameson, Florent Michelot

Bibliographic record

VenueRevue internationale des technologies en pédagogie universitaire · 2020
Typearticle
Languagefr
FieldComputer Science
TopicArtificial Intelligence in Education
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

L'élaboration du Cadre de référence de la compétence numérique (Ministère de l'Éducation et de l'Enseignement supérieur, 2019) s'inscrit dans le cadre du Plan d'action numérique en éducation et en enseignement supérieur (PAN).Ce plan préconise la vision d'une intégration efficace et d'une exploitation optimale du numérique au service de la réussite de toutes les personnes, qui leur permettent de développer et de maintenir leurs compétences tout au long de leur vie.La première mesure du PAN a une portée structurante en visant l'établissement et l'opérationnalisation d'un cadre de référence.

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)0.013
metaresearch head score (Gemma)0.035
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: Other
Teacher disagreement score0.309
Threshold uncertainty score0.614

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0050.019
Scholarly communication0.0130.012
Open science0.0020.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0140.003

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 head0.051
GPT teacher head0.297
Teacher spread0.246 · 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

Citations1
Published2020
Admission routes3
Has abstractyes

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