Concepts of Bias and Appointments to the Governing Council of the Canadian Institutes of Health Research
Bibliographic record
Abstract
In October 2009, the academic health research community and the pharmaceutical industry were brought closer together with the appointment of Dr. Bernard Prigent, vice-president of Pfizer Canada, to the Governing Council of the Canadian Institutes of Health Research (CIHR). This bridging of the two worlds has stirred up considerable debate before the House of Commons Standing Committee on Health, in letters to CMAJ and in an online petition that garnered more than 4400 signatures. There are at least two distinct and vocal camps in the debate: those categorically in favour (including the federal minister of health and the president of CIHR) and those opposed to the appointment of someone from the pharmaceutical industry (including several senior Canada Research Chairs with a specialization in ethics and senior persons within CIHR). There are also some who support the appointment of a person with professional ties to the pharmaceutical industry, but not to this particular company (Pfizer) because of its history of ethical and legal violations.
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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 | 0.189 | 0.265 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.042 | 0.124 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.032 | 0.030 |
| Insufficient payload (model declined to judge) | 0.008 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".