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Record W6930627104 · doi:10.5281/zenodo.14766543

Utiliser Microsoft Teams avec des petits groupes d'étudiants : guide de démarrage pour le personnel enseignant

2020· article· fr· W6930627104 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typearticle
Languagefr
FieldSocial Sciences
TopicCollaborative Teaching and Inclusion
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSection (typography)Microsoft excelMicrosoft Office

Abstract

fetched live from OpenAlex

Guide à l’intention du personnel enseignant désirant contacter un petit groupe d’étudiants (moins de 20) pour des besoins spécifiques de soutien ou de travail collaboratif avec vidéoconférence. Section 1 - Microsoft Teams en ligne Section 2 - Installation de Microsoft Teams Section 3 - Connexion Section 4 - Interface Teams Section 5 - Votre profil Section 6 - Clavardage Section 7 - Équipes Section 8 - Planifier une rencontre Teams dans Microsoft Outlook Section 9 - Vidéoconférence Section 10 - Dépannage audio Section 11 - Besoin d'aide Section 12 - Annexe

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)0.006
metaresearch head score (Gemma)0.011
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: Methods · Consensus signal: Methods
Teacher disagreement score0.065
Threshold uncertainty score0.217

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0650.060

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 head0.043
GPT teacher head0.274
Teacher spread0.231 · 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
GenreMethods

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
Published2020
Admission routes1
Has abstractyes

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