Les compétences technologiques des rédacteurs professionnels : résultats d’un sondage pancanadien et analyse des cours offerts dans les universités ontariennes
Bibliographic record
Abstract
Cet article porte sur les compétences technologiques des rédacteurs professionnels. Les résultats de notre sondage pancanadien révèlent que la pratique de la rédaction professionnelle requiert des compétences technologiques variées : production de documents adaptés à divers supports, connaissance de l’hypertexte et des nouveaux genres du web, utilisation de nombreuses TIC. Ces constats sont mis en relation avec une analyse de la place des technologies dans les cours de rédaction de niveau universitaire. À partir des données compilées dans les universités ontariennes, nous montrons que les cours de rédaction permettent aux futurs rédacteurs professionnels d’acquérir en partie les compétences technologiques attendues.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".