Guide des technologies et outils efficaces pour le perfectionnement et la formation en ligne du personnel [ressource électronique]
Bibliographic record
Abstract
Community Literacy of Ontario (CLO) se fait souvent demander quel type de technologie elle utilise pour offrir de la formation en ligne. Cela l'a amene a se demander quels autres types de possibilites d'apprentissage en ligne etaient disponibles en Ontario, et comment elle pourrait communiquer plus globalement ses connaissances et son experience a d'autres organismes. Ce guide de ressources a ete produit avec les objectifs suivants : effectuer une recherche sur les technologies efficaces utilisees actuellement pour offrir du perfectionnement professionnel et de la formation au personnel dans le cadre du reseau de prestation d'Emploi Ontario, effectuer une recherche sur les technologies efficaces utilisees pour offrir de la formation au personnel dans le secteur plus elargi de l'education et de la formation, rediger un guide de reference qui offre un resume concis de la recherche et fournir des exemples concrets de technologies de formation en ligne pour le personnel. - Tire du doc.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.008 |
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".