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Record W4387524632 · doi:10.18162/ritpu-2023-v20n2-07

Moving Forward After COVID-19: New Directions for Teaching and Course Design in Higher Education

2023· article· en· W4387524632 on OpenAlexaffvenueabout
Nadia Naffi, Ann-Louise Davidson, Laura R. Winer, Brian Beatty, Aline Germain‐Rutherford, Rula L. Diab, Teresa Focarile, Danny Rukavina, David Hornsby, Shantell Strickland-Davis, Saouma BouJaoude, Stéphanie Côté, Geneviève Raîche-Savoie, Jean‐François Racine, Louis Camara, Nathalie Duponsel, Valentine Kropf

Bibliographic record

VenueRevue internationale des technologies en pédagogie universitaire · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsUniversité TÉLUQCarleton UniversityMcGill UniversityConcordia UniversityUniversity of OttawaUniversité Laval
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Course (navigation)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakMathematics educationComputer sciencePsychologyVirologyMedicineEngineeringAerospace engineeringInternal medicineOutbreak

Abstract

fetched live from OpenAlex

Aller de l'avant après la pandémie de la COVID-19 : nouvelles orientations pour l'enseignement et la conception des cours dans l'enseignement supérieur

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)0.103
metaresearch head score (Gemma)0.103
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.103
Threshold uncertainty score0.546

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1030.103
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0060.008
Scholarly communication0.0230.020
Open science0.0050.009
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0150.003

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 head0.091
GPT teacher head0.376
Teacher spread0.285 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations2
Published2023
Admission routes3
Has abstractyes

Explore more

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