Optimizing the Transformation of Knowledge Dissemination: Towards a Canadian Research Strategy: Preliminary Results
Bibliographic record
Abstract
Academic researchers are the major actors in the scholarly communication system and, as such, it is extremely important that any research being conducted in this area be guided by their needs. This study assembles a diverse panel of Canadian academic researchers in order to define a research strategy for the dissemination of scholarly knowledge in Canada that is defined by relevance to the research community. The major research question addressed here is the nature of a research agenda for the dissemination of scholarly research in Canada. These results so far reflect a substantially different approach to defining a research agenda for the dissemination of scholarly research than those outlined in the past.Les chercheurs académiques sont les acteurs principaux du système de communication universitaire et en tant que tel, il est extrêmement important que toute recherche poursuivie dans ce domaine soit guidée par leurs besoins. Cette étude rassemble un groupe de chercheurs universitaires canadiens dans le but de définir une stratégie de recherche pour la diffusion des connaissances académiques canadiennes et qui sera considérée comme pertinente par le milieu de la recherche. La principale question de recherche soulevée ici est la nature de l’agenda de recherche pour la diffusion de la recherche universitaire au Canada. Jusqu’à présent, ces résultats reflètent une approche considérablement différente pour définir un agenda de recherche pour la diffusion de la recherche académique par rapport aux agendas produits dans le passé.
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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 | 0.052 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".