Synthèse d’idées et de travaux à propos de la coélaboration/création de connaissances et du Knowledge Forum \n
Bibliographic record
Abstract
Le Knowledge Building (Scardamalia & Bereiter, 1994) – traduit par coélaboration de connaissances au Québec – est un modèle théorique et pédagogique important du domaine de l’apprentissage collaboratif supporté par l’ordinateur (computer-supported collaborative learning). Il est aussi un des modèles fondateurs du champ des sciences de l’apprentissage (learning sciences) (Sawyer, 2005). De façon concomitante au développement conceptuel de la coélaboration de connaissances, un développement technologique s’est effectué. Les affordances (Allaire, 2006; Gaver, 1991) du logiciel Computer-Supported Intentional Learning Environments (CSILE), qui a migré vers le Knowledge Forum à la fin des années 1990 dans le cadre des activités du TeleLearning Network of Centres of Excellence (Canada), ont été délibérément réfléchies de telle sorte à supporter les individus et les communautés prenant part à des travaux de coélaboration de connaissances. Ce texte présente le concept et les principes de coélaboration de connaissances, ses ancrages dans la recherche sur l’écriture ainsi qu’un compte-rendu de récents travaux.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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.
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".