The Ontario election: Healthcare on life-support
Bibliographic record
Abstract
Guest: Toronto Star health reporter Megan OgilvieOntario is racing towards a snap election on February 27 and for a lot of voters, two issues loom well above the rest: housing and healthcare. Both are at breaking point and both are dominating party platforms. As part of the Star's pre-election coverage, we're delving into these issues. Where do things really stand, are any of the candidates offering actual solutions, and what should you, the voters, be thinking about as you head to the polls? Today's episode will focus on healthcare. With overflowing ERs, health-care worker burn-out and more than two million people without a family doctor, Ontario's healthcare has been in trouble for years. Can anyone bring it back on track? Audio sources: Global News, CTV, CBC, YoutubeThis episode was produced by Paulo Marques and Saba Eitizaz
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.051 |
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; both teacher heads agree on what is shown here.
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".