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Record W3108076120 · doi:10.18162/ritpu-2020-v17n2-01

Le numérique et l’enseignement au temps de la COVID-19 : entre défis et perspectives – Partie 1

2020· article· fr· W3108076120 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueRevue internationale des technologies en pédagogie universitaire · 2020
Typearticle
Languagefr
FieldComputer Science
TopicEducational Innovations and Technology
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPolitical scienceCoronavirus disease 2019 (COVID-19)Philosophy

Abstract

fetched live from OpenAlex

En raison de la situation mondiale lie la COVID-19, et selon les donnes publies par l'UNESCO (2020a), quelque 1,6 milliard d'apprenants dans le monde, du prscolaire l'universit, n'ont pas t en mesure, un moment ou un autre, de frquenter leur tablissement d'enseignement depuis le dbut de la pandmie. Concrtement, ce chiffre inquitant confirme que plus de 94 % des apprenants scolariss sur Terre ont t touchs par cette crise sanitaire. ce jour, plus de 190 pays ont ordonn la fermeture de l'ensemble de leurs tablissements d'enseignement, des coles primaires aux universits, un moment ou un autre, au cours des derniers mois. Et avec l'arrive de la deuxime vague de COVID-19, les tablissements d'enseignement universitaires se sont une fois de plus dirigs vers l'enseignement distance, et ce, pour plusieurs mois.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.871
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.312
Teacher spread0.266 · 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