Bibliographic record
Abstract
Most Canadians think that "medicare"/our healthcare system (for the differentiation is certainly not clear) is "public," meaning universal and pre-paid by their taxes. Those who have heard of the five conditions of the Canada Health Act (CHA), or at least the phrase, "public administration," are doubly confirmed in their belief. It comes as a surprise to concerned citizens to learn that, to get federal funding, a province has to set up a "single payer" for health services to fall under medicare, that is, hospitals and physicians. Then, if more information is introduced to distinguish between funding and delivery of services, and it is stated how the former is public, while the latter is mainly private, the audience starts challenging the speaker. Explaining that the delivery of services is private because doctors or nurses are not civil servants, for example, comes across as one more great Canadian fiction. "After all, they are fully remunerated by public funds--my taxes." All of this to recognize that, in Canada, discussions around the public/private divide, from whatever angle, are surrounded by preconceived, often common-sense, ideas rejected mainly by students of healthcare systems.
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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.027 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.081 | 0.064 |
| Insufficient payload (model declined to judge) | 0.009 | 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".