Bibliographic record
Abstract
Voici le troisième numéro de la revue Les Annales de QPES. Comme les deux précédents, il fait suite au Xe colloque Questions de Pédagogies dans l’Enseignement Supérieur (QPES), qui s’est tenu à Brest en juin 2019. À titre de rappel, le colloque avait pour thème « (Faire) coopérer pour (faire) apprendre ? ». Les sept articles présentés dans ce 3e numéro abordent bien sûr tous ce thème, et ils le font en mettant en relief les espaces d’apprentissage proposés pour susciter la coopération (Arendale, 2020 ; Salomone, 2017) ou la collaboration (Pluta et al., 2013 ; Sisman et al., 2019), ou encore le développement de compétences (Lacasse et al., 2017 ; Sklar, 2015), la motivation (Bédard, 2020 ; Cummings et Sheeran, 2019 ; Parent, 2014) ou l’engagement (Albinson, 2019 ; Bédard et al., 2012). C’est finalement cette orientation autour des pédagogies dites actives (Kirschner et Hendrick, 2020 ; Tong et al., 2018) qui réunit ces différents textes.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.209 | 0.097 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".