Devolution - A Solution for Ontario: Could the Lone Wolf Lead the Pack?*
Bibliographic record
Abstract
In response to what we describe as the "accountability gap" in healthcare, nine provinces have embraced the devolution of management responsibility and authority from central government administrations to regional health authorities. Ontario, Canada's most populous province, is the lone wolf. This commentary focuses on the consequences of Ontario's reluctance to adopt devolution. The authors argue that devolution is an important first step in improving the lines of accountability within publicly funded healthcare; however, as a reform initiative, devolution must form part of a series of interlocking initiatives. These complementary reforms include refocusing the debate from funding (money) to governance, clarifying the governance roles of both the federal and provincial governments and developing an incentive- and information-based system that is geared more to rewarding gains in healthcare outcomes as opposed to the delivery of health services. With a new government in Ontario, there is now a window of opportunity to capitalize on the experiences and failures of other provinces and for Ontario to emerge as the leader of the pack, rather than the lone wolf.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.034 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".