Bibliographic record
Abstract
Pour des raisons de performance, innovation ou compétitivité, les connaissances sont devenues une ressource de première importance dans les organisations de l’économie du savoir. Gérer les connaissances explicites et tacites représente une stratégie ayant ses propres concepts, méthodes, outils, qui attirent l’attention des spécialistes. En appliquant une analyse à un corpus d’articles scientifiques, cette étude se propose d’identifier les domaines traitant ou mettant en application la gestion des connaissances, afin de dresser un inventaire de ses champs d’étude et/ou d’application et contribuer ainsi à une meilleure compréhension de ses bénéfices. Ce texte présente des résultats préliminaires : douze domaines d’étude identifiés comme abordant la gestion des connaissances, à comparer aux résultats de la deuxième étape de la recherche (en cours), pour finalement élaborer un portrait de la gestion des connaissances et des aires d’études reliées.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".