Faut-il favoriser rapidement l’enseignement 2.0 dans les écoles? ou les dommages collatérauxd’une implantation sauvage des NTICE
Bibliographic record
Abstract
L’auteur analyse l’echec du projet d’implantation du TBI (tableau blanc interactif) dans toutes les ecoles du Quebec et il en devoile les causes. Il regarde ensuite les chances de succes de l’implantation massive de la tablette numerique. Il en deduit que, pousse par les injonctions des industriels du materiel, le projet a peu de chance de reussir parce que l’on n’a pas tenu compte du contexte social de l’operation qui doit etre un processus de coconstruction entre tous les partenaires. En education, les professeurs sont souvent les victimes des “inventions” des technologues de l’education qui sont si prompts a implanter leurs nouveaux systemes. Heureusement, le projet cree la controverse au Quebec et le debat public est lance.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".