Bibliographic record
Abstract
Discussion of conflict of interest is not new to this journal; one variation on the theme concerned the appointment of a pharmaceutical industry executive to the governing council of the Canadian Institutes of Health Research. 1,2 We now turn to a similar controversy that erupted recently with respect to the International Development Research Centre (IDRC) when it came to public attention that the chair of their board of governors had an affiliation with the tobacco industry. IDRC, a Crown agency, funds tobacco control programs among many other initiatives. Their board chair, former Cabinet minister Barbara McDougall, had been on the board of directors of Imperial Tobacco Canada since March 2004 and had chaired its corporate social responsibility committee. This was not declared in the IDRC’s announcement of McDougall’s appointment to their board in January 2007, or when she became board chair in December of that year. The inappropriateness of serving on these two boards simultaneously is self-evident. But what can get lost in the furore of conflict-of-interest allegations is an informed understanding of the collateral damage caused by such conflicts. What has also been missed to some extent is an understanding of how this particular case exemplifies the insidious challenges faced these days by the tobacco control community. First, to review the situation briefly. The IDRC “works in close collaboration with researchers from the developing world in their search for the means to build healthier, more equitable, and more prosperous societies. ” 3 This mandate comfortably accommodates the IDRC’s Research for International Tobacco Control (RITC) program, which provides grants for multidisciplinary research on tobacco control in developing countries. RITC has received funding from the UK Department for
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.348 | 0.137 |
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".