Bibliographic record
Abstract
Cet article dresse un portrait empirique du courtage de connaissances a l’ere du numerique. Au-dela de la rhetorique et de l’anecdotique en presence, il apporte des reponses a deux familles de questions : comment fonctionne le courtage de connaissances et quels sont ses impacts sur la performance des interventions publiques en sante au Canada ? Pour ce faire, l’auteur s’appuie sur un sondage realise aupres d’un echantillon representatif de courtiers de connaissances utilisant les technologies numeriques au Canada. L’article caracterise les activites de courtage de connaissances (etapes, comportements, interactions, etc.), les attributs individuels des courtiers (âge, genre, experience, formation, preferences, etc.), les attributs des connaissances echangees, ainsi que les impacts du courtage de connaissances. Les resultats montrent la complexite du metier de courtier de connaissances et demontrent que les impacts du courtage de connaissances sont proportionnels a 1) la qualite des connaissances echangees, 2) l’intensite des interactions initiees avec les partenaires impliques et 3) la connectivite des courtiers dans des reseaux sociaux web 2.0.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".