GAME CENTER: THREE WEBSITES INTERTWINE TO ENHANCE TEACHER’S COLLABORATIVE TEAMWORK TO CREATE ACTIVITIES AND GAMES FOR ELEMENTARY AND SECONDARY PUPILS
Bibliographic record
Abstract
Margot KASZAP is professor in Education and a specialist of qualitative methodologies at Laval University. She is a coresearcher in the elderly literacy research team Alpha-santé and a member of the Canadian Research Team SAGE-21, which develop learning games and simulations with new technologies. As a main researcher she obtain a grant from the Office of Learning Technologies of Canada. She participate to develop the new aspects in social science in the PISTES web site. She gave more than 80 communications, published 4 chapters in collective book writing, 14 scientific articles, numerous reports and teaching supply for hospital and courses. Louise GUILBERT is a retired titular professor in Education and a specialist of critical thinking methods at Laval University. She developped the web site PISTES and Chantier pédagogique, for which she received the Minister of Education Award. In her career she wrote many books, numerous articles and reports. She devoted
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".