Évaluation de la qualité de la connaissance dans une perspective délibérative
Bibliographic record
Abstract
Cet article propose une vision des démarches d’évaluation de l’adéquation de la connaissance scientifique dans des situations d’incertitude forte et irréductible en recourant à des processus délibératifs élargis. Dans l’optique de la Science Post-Normale, la démarche s’appuie, d’un point de vue épistémologique, sur l’articulation des approches scientifiques et de sciences sociales pour définir la qualité intrinsèque de la connaissance et sa pertinence dans des contextes sociaux, culturels et politiques différents.Cet article présente un outil de contrôle de la qualité de la connaissance et de « bonnes pratiques » scientifiques (NUSAP). La question de la pertinence de la connaissance, qu’elle soit scientifique ou vernaculaire, s’intègre dans un processus multidimensionnel délibératif, associant divers acteurs, critères, échelles, sites… et portant sur les indicateurs et sur les orientations politiques à travers la Foire Kerbabel™ aux Indicateurs et la Matrice Kerbabel™ de Délibération.
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 | 0.116 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".