Investing in an efficient and transparent research ecosystem in Canada - Investir dans un écosystème de recherche efficace et transparent au Canada
Bibliographic record
Abstract
<p>In 2021, the Canadian Research Knowledge Network (CRKN) in partnership with the Digital Research Alliance of Canada consulted with <a href="https://www.morebrains.coop/">MoreBrains Cooperative</a>, international experts in PIDs and PID Strategy development, to assess the state of PIDs in Canada and laying the foundation for a national strategy. This 2024 report, undertaken on behalf of and reviewed by the <a href="https://www.crkn-rcdr.ca/en/persistent-identifier-pid-governance" target="_blank" rel="noopener noreferrer">Canadian Persistent Identifier Advisory Committee (CPIDAC)</a>, synthesizes findings from three phases of research, consultation, and analysis. It refines the vision, priorities, and key PIDs while providing actionable recommendations based on extensive stakeholder engagement. MoreBrains highlights governance, technical capacity, leadership, and community engagement as critical to advancing PID adoption and building a more efficient, trustworthy, and interoperable research ecosystem.</p> <p>***************************</p> <p>En 2021, le Réseau canadien de documentation pour la recherche (RCDR) en partenariat avec l’Alliance de recherche numérique du Canada a fait appel à <a href="https://www.morebrains.coop/">MoreBrains Cooperative</a>, 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 établir les bases d’une stratégie nationale. Ce rapport de 2024, réalisé au nom du <a href="https://www.crkn-rcdr.ca/fr/gouvernance-identifiants-perennes-pid">Comité consultatif canadien sur les identifiants pérennes (CCCPID)</a> et examiné par celui-ci, synthétise les résultats de trois phases de recherche, de consultation et d’analyse. Le rapport peaufine la vision, les priorités et les principaux PID tout en proposant des recommandations concrètes basées sur une large mobilisation des parties prenantes. MoreBrains souligne que la gouvernance, la capacité technique, le leadership et l’engagement de la communauté sont essentiels pour favoriser l’adoption des PID et bâtir un écosystème de recherche plus efficace, fiable, et interopérable.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".