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Record W2312333787 · doi:10.15210/interfaces.v2i1.6381

ESCale - environnement de formation professionnelle technique assistée par ordinateur : une expérience pilote menée au Québec

2015· article· fr· W2312333787 on OpenAlexaboutno aff
Gilberto Lacerda Santos, Richard Gagnon

Bibliographic record

VenuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicInformation Technology and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

ESCale é um ambiente aberto e informatizado de formação profissional que obedece ao princípio de que o elemento mais qualificado para uma certa tarefa, seja ele humano, material ou software é o responsável por conduzir a relação educativa. Dois eixos condicionam a ação pedagógica do ambiente: uma estruturação conceitual, sistêmica e técnico-científica dos saberes do professor e uma abordagem pedagógica centrada no aluno. Experimentações parciais confirmaram a adequação e a pertinência do ambiente ESCale.Résumé: ESCale est un environnement ouvert de formation professionnelle technique assistée par ordinateur. Il obéit au principe que l’élément de l’environnement le plus qualifié pour une tâche donnée, qu’il soit humain, matériel ou logiciel, est responsable de la mener à bien mais que l’autorité ultime est celle de l’enseignant. Deux axes conditionnent son action pédagogique : une structuration conceptuelle, systémique et technoscientifique des savoirs d’enseignement et une approche pédagogique centrée sur l’apprenant. Des expérimentations partielles ont confirmé le bien-fondé et la pertinence de l’environnement.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.733

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.079
GPT teacher head0.388
Teacher spread0.309 · 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 designObservational
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

Citations0
Published2015
Admission routes1
Has abstractyes

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Same venuePortuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT)Same topicInformation Technology and LearningFrench-language works237,207