Towards a national PID strategy for Canada - Vers une stratégie nationale sur les PID pour le Canada
Bibliographic record
Abstract
In 2021, on behalf of the Canadian Persistent Identifier Advisory Committee (CPIDAC), the Canadian Research Knowledge Network (CRKN) in partnership with the Digital Research Alliance of Canada consulted with MoreBrains Cooperative, international experts in PIDs and PID Strategy development, to assess the situation of PIDs in Canada and to provide a snapshot of our starting point as we embark on developing a national strategy. MoreBrains outlined a foundation-laying approach and identified information gaps to fill before a complete PID strategy could be laid out. Participants included university leadership, researchers, professional associations, federal and provincial funders, technical experts, and more. *************************** En 2021, au nom du Comité consultatif canadien sur les identifiants pérennes (CCCPID), le Réseau canadien de documentation pour la recherche (RCDR) en partenariat avec l’Alliance de recherche numérique du Canada a fait appel à MoreBrains Cooperative, des spécialistes internationaux en matière de PID et d’élaboration de stratégies sur les PID, afin d’évaluer la situation des PID au Canada et de donner un aperçu de notre point de départ, au moment d’entreprendre la création d’une stratégie nationale. MoreBrains a défini une approche visant à établir des fondations et a relevé les lacunes en matière d’information à combler avant de pouvoir élaborer une stratégie complète sur les PID. Au nombre des participants figuraient des dirigeants d’universités, des chercheurs, des associations professionnelles, des bailleurs de fonds fédéraux et provinciaux, des experts techniques, etc.
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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 | 0.029 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.022 | 0.010 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".