Bibliographic record
Abstract
From the Deputy Minister of Health Premier, Less than two years ago you honoured me with an appointment as Deputy Minister of Health.Although we have spoken several times since, this is the first occasion on which I have felt an issue to be of such importance that I am setting my views out in writing.Oddly, my note is not prompted by the impasse in negotiations with the Medical Association or by the recent labour disputes in the hospital sector.Nor is it prompted by the media obsession with labelling every health story, a health crisis.The problems we face in implementing the health reform program are well known to you.Rather, I read something sensible and wanted to pass it along.This afternoon as I clawed my way through the weekend pile of material, a task familiar to you, I came upon a first-rate piece of work by three of Canada's most insightful analysts (Leatt, Pink and Guerriere) on the health policy scene.Don't be alarmed, Pink is his name not his political persuasion!These are not simply academics tendering advice from the safety of the university.These are also thinkers and doers willing to manage and plan real health services.Of importance is their review of the experience of other jurisdictions and the lessons to be drawn.The article is attached and is worth a read but that is not the point of this note, merely the spark.
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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.038 | 0.034 |
| Insufficient payload (model declined to judge) | 0.034 | 0.029 |
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".