Strengthening Canadian Democracy
Bibliographic record
Abstract
Public Policy is an independent, national, nonprofit organization. IRPP seeks to improve public policy in Canada by generating research, providing insight and sparking debate that will contribute to the public policy decisionmaking process and strengthen the quality of the public policy decisions made by Canadian governments, citizens, institutions and organizations. IRPP's independence is assured by an endowment fund established in the early 1970s. Fondé en 1972, l’Institut de recherche en politiques publiques (IRPP) est un organisme canadien, indépendant et sans but lucratif. L’IRPP cherche à améliorer les politiques publiques canadiennes en encourageant la recherche, en mettant de l’avant de nouvelles perspectives et en suscitant des débats qui contribueront au processus décisionnel en matière de politiques publiques et qui rehausseront la qualité des décisions que prennent les gouvernements, les citoyens, les institutions et les organismes canadiens. L’indépendance de l’IRPP est assurée par un fonds de dotation établi au début des années 1970. This publication was produced under the direction of Geneviève Bouchard, Research Director, IRPP. The manuscript was copy-edited by Mary Williams, proofreading was by Zofia Laubitz, production was by Anne Tremblay, art direction was by Schumacher Design, and printing was by Impressions Graphiques. Copyright belongs to Michael Pal and Sujit
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.005 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.079 | 0.009 |
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".