Bibliographic record
Abstract
Now that Ottawa has left health care to the provinces, what is the future for Canadian health care in a decentralized federal context? Is the Canada Health Act dead? Health Care Federalism in Canada provides a multi-perspective, interdisciplinary analysis of a critical juncture in Canadian public policy and the contributing factors which have led to this point. Social scientists, legal scholars, health services researchers, and decision-makers examine the shift from a system where Ottawa has played a significant, sometimes controversial role, to one where provinces have more ability to push health care design in new directions. Will this change inspire innovation and collaboration, or inequality and confusion? Providing an up-to-date analysis of health care policy and intergovernmental relations at a crucial time, Health Care Federalism in Canada will be of interest to anyone concerned with the current dynamics and future potential of Canadian health care. Contributors include Greg Marchildon (Canada Research Chair at the Johnson-Shoyama Graduate School of Public Policy in Saskatchewan), Ken Boessenkool (public affairs strategist and former political advisor to Stephen Harper), Adrian Levy (Professor and Head, Department of Community Health and Epidemiology at Dalhousie University), Boris Sobolev (Canada Research Chair at the School of Public and Population Health, University of British Columbia), Gail Tomblin Murphy (Director, WHO Collaborating Centre for Health Workforce Planning and Research), and David Haardt (Department of Economics, Dalhousie University).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.028 | 0.009 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".