Bibliographic record
Abstract
Ce chapitre vise à considérer la valorisation de la recherche doctorale et dans la recherche doctorale comme une étape majeure, et pourtant sous-estimée. En nous appuyant sur des expériences, qu’elles soient personnelles ou non, nous proposons une vision de ce concept de valorisation, que nous positionnons comme intrinsèquement liée à la question de l’impact, ou plus exactement des différentes formes d’impact. Nous proposons de considérer la valorisation comme un ensemble d’écosystèmes, duquel il nous semble nécessaire de se saisir, et ce dès le parcours doctoral, dans une logique de conquête prudente et progressive des différents espaces (académique, médiatique, voire politique). Enfin, nous ouvrons cette réflexion avec un regard sur les défis de la mesure de toutes les formes d’impact, parmi lesquelles l’impact académique, mais aussi l’impact managérial. Ce travail s’achève sur quelques conseils à l’usage des doctorants qui souhaitent valoriser le fruit de leur recherche doctorale.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.027 | 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".