Les retraites de rédaction structurées auprès des doctorant[e]s : Quelles conditions favorisent une expérience de rédaction légitime, productive et plaisante?
Bibliographic record
Abstract
La rédaction de la thèse est problématique pour nombre de doctorant[e]s. Pour les soutenir, la retraite de rédaction structurée semble une stratégie intéressante, mais peu de recherches ont tenté de la décrire. Cet article présente des résultats issus d’une étude mixte (N = 210), combinant questionnaires et entretiens pour mesurer et comprendre les effets des retraites Thèsez-vous en vue 1) d’en recenser les bénéfices, soit favoriser la productivité, légitimer la rédaction et vivre une expérience plaisante, et 2) d’identifier les conditions qui favorisent ces bénéfices. Ultimement, cet article contribue à l’identification de stratégies pour repenser et améliorer l’encadrement de la rédaction de thèse. Mots-clés : rédaction, retraite de rédaction, bénéfices, flow, productivité scientifique
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 | 0.029 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".