Beyond the Binary: Acknowledging Complexity, Enabling Innovation, Preserving the Positive
Bibliographic record
Abstract
In reviewing feedback on our article, we are reassured by the unanimity of concern about the current situation. However, there remains much oversimplification about what is meant by "public" and "private," which undermines clearer thinking and innovation in practice. The confusion that results needs all our efforts to be removed. Canada's health system today holds much-deserved pride and praise. However, preserving and sustaining those accomplishments is in doubt, due in part to drivers of population need and change, which cannot be avoided, and in part to, in our view, somewhat misplaced rigidity and misunderstanding about the current situation and options going forward. We observe greater flexibility and innovation in other high-income countries. We urge learning from those innovations with a more open mind. May these exchanges move us a bit further along that path.
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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 | 0.063 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.061 |
| Scholarly communication | 0.029 | 0.051 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.012 | 0.024 |
| 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".