To Build a Wooden Horse … Integrating Drugs into the Public Health System
Bibliographic record
Abstract
The Canadian debate surrounding pharmacare follows some strange rules. On the one hand, there is a strong and widely shared belief that drugs ought to be among the benefits of the public health system, along with the other core services--hospitals and physicians. On the other hand, it is hard not to see the many obstacles to such a project, including the costs associated with drug consumption and the indifference of a large part of the population, which seems to be coming to terms with the fragmentary coverage it already has. Champions prepared to defend the cause of a universal and public system are but a few, and the sour experience of the National Forum on Health (1994-97), which proposed nothing short of comprehensive public coverage, is there as a reminder that a frontal attack is pointless--it looks too expensive, too complicated, too difficult. As did the Greek kings outside Troy, when Ulysses suggested to them that they build a wooden horse, the experts endlessly debate the stratagem that will make it possible to create a public system, without arousing the suspicions of the policy-makers or even the population. The lead paper by Morgan and Willison is no exception: once again, the idea is to achieve pharmacare without alerting potential opponents.
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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.029 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.074 | 0.046 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".