Forum on Public Policy 1 Ethical Transparency and Government Regulation of Canada's Medical Research Industry
Bibliographic record
Abstract
Medical research and development (R&D) is an area where the interests of private sector firms often conflict with those of governments. 1 More precisely, the private sector firms conducting the bulk of medical R&D are motivated by the ethical standards of the marketplace. 2 These standards differ from those of government which, in Canada, is an advocate for patients as well as having monopoly control of the health care system through the publicly-funded, provincially-run Medicare and Pharmacare systems. In this environment, there is a strong incentive for government to require a high level of ethical transparency in the regulatory filings that firms conducting medical research are required to provide. However, at least since Nancy Olivieri versus Apotex, there has been accumulating evidence that current levels of disclosure still do not make it possible to separate legitimate medical research from a corporate strategy of marketing patent protected medical products to physicians.
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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 | 0.049 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.038 | 0.011 |
| Open science | 0.010 | 0.011 |
| Research integrity | 0.134 | 0.039 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".