Comment développer ses compétences en TIC? L’expérience des personnes expertes de divers milieux du Réseau CompéTICA
Bibliographic record
Abstract
Cet article identifie les attitudes, les défis et la formation qui ont permis à 42 personnes expertes du Canada atlantique et partenaires du Réseau CompéTICA d’acquérir des compétences en TIC. Les personnes expertes proviennent des milieux de l’éducation, de l’enseignement postsecondaire, des entreprises privées, du gouvernement et d’organismes à but lucratif, sans but lucratif et communautaires. À travers une méthode Delphi, il est démontré que la formation informelle est un facteur important dans la maitrise des compétences en TIC et que des attitudes spécifiques peuvent aider à les acquérir. À la suite des résultats obtenus, un cadre conceptuel est présenté.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 teacher head, 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".