Bibliographic record
Abstract
The twenty-ninth edition of How Ottawa Spends focuses on the policies of the Harper government and the course of federal-provincial relations. Leading scholars of Canadian public policy explore several key policy areas, including fiscal balance in the federation, tax policy, regulatory capacity, the federal funding of territorial and northern Aboriginal governments, child care policy, higher education policy, telecommunications policy, and the rapid appearance and disappearance of the federal social economy initiative - i.e., "how Ottawa doesn't spend." Contributors include Frances Abele (Carleton & IRPP), Cheryl N. Collier (Carleton), Geoffrey Hale (University of Lethbridge), Walter Hettich (emeritus, California State), Edward T. Jackson (Carleton), Rianne Mahon (Carleton), Allan M. Maslove (Carleton), Clara Morgan (Carleton), Michael J. Prince (University of Victoria), Richard Schultz (McGill), Robert Slater (Carleton), Barry Stemshorn (University of Ottawa), and Stanley L. Winer (Carleton). An examination of federal and provincial government responsibilities with respect to native peoples, these essays deal with the most appalling "political football" in Canadian politics. Specially commissioned experts in the field write on topics such as fiscal, legal and constitutional issues, and examine the circumstances of specific native groups in Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".