Bibliographic record
Abstract
This is the second edition of our annual volume of commentary and assessment of Canadian and related comparative innovation, science and environment (ISE) policies and institutions.It emerged initially out of a broader body of research and teaching at the Carleton Research Unit on Innovation, Science and Innovation (cruise) and the School of Public Policy and Administration at Carleton University.Aimed at an audience of interested and informed Canadians involved in, or affected by, this crucial realm of Canadian policy, politics and governance, the book examines the ISE policy priorities of the federal government.Chapters are also devoted to broader areas of federal-provincial and cities/communities involvement in these fields as well as the crucial international and comparative dimensions which impact on Canada.We are especially indebted to our roster of contributing academic and other expert research authors from across Canada for their insights and for their willingness to contribute to this work.The book is structured on the basis of a general call for chapters in the ISE field, a number of which were then selected for inclusion by the editor.In this volume and in later ones our aim is to involve academics from a variety of disciplines as well as doctoral students from across Canada doing advanced research in the ISE field and also knowledgeable practitioners from the public and private sectors.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.309 | 0.106 |
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".