Where There’s Smoke, There’s Pfizer
Bibliographic record
Abstract
In 2009, Bernard Prigent (Vice President and Medical Director of Pfizer Canada and registered Pfizer lobbyist), became a member of CIHR’s Governing Council. At that time, Prigent’s job included influencing the allocation of health research dollars and CIHR was among the target organizations. Concerns about Prigent's conflict of interest were raised within and outside CIHR. The pivotal question was: can a senior officer and lobbyist for Pfizer Canada represent the public interest as a member of the Governing Council of an organization he is paid to influence? When they heard about Prigent’s appointment, Baylis and Downie tried to get the appointment decision reversed. They communicated with the CIHR Ethics Office, CIHR Ethics Designates, CIHR Standing Committee on Ethics, as well as the ethics member of the CIHR Governing Council. When this was ineffective, they launched a public petition addressed to the government. They attempted to generate public engagement through national media. One of them appeared before the Parliamentary Standing Committee on Health arguing that the appointment represented an unmanageable conflict of interest and should be reversed. Unfortunately, these interventions were not successful and Prigent remained on the Governing Council. Here, Baylis and Downie reflect on lessons learned about bioethics advocacy through this case.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".