Industry and the academy: conflicts of interest in contemporary health research.
Bibliographic record
Abstract
The case of Dr. Nancy Olivieri, the Hospital for Sick Children (HSC), the University of Toronto, and Apotex Inc. (hereinafter the "Olivieri case") is critically important to an understanding of the issues central to contemporary health research and the safety of research participants. First, the case illustrates the huge stakes in such research – not only billions of dollars, but the health of Canadians. Second, the case played out at a crucial time in the history of the regulation of health research. Like other recent high-profile cases, it challenged the ways in which research is governed at the local and national levels and fuelled calls for significant governance reform. Finally, it is relevant not only nationally but in individual communities right across the country. What happened in Toronto could have happened (and could still happen) anywhere in Canada. To pursue the promises and avoid the perils of contemporary health research, it is essential to attend to the lessons of this case.
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 | 0.027 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.025 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 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".