Law, Culture and Reprisals: A Qualitative Case Study of Whistleblowing & Health Canada's Drug Approval Process
Bibliographic record
Abstract
Countries around the world consider whistleblowing a reliable warning system for corruption and regulatory failure because whistleblowers are usually employees who have in-depth knowledge of complex systems and organizations often impenetrable and incomprehensible to outsiders.Why then do whistleblowers, these harbingers of wrongdoing, suffer censure and reprisals?The search for an answer to this question sparked this case study of a whistleblower's concerns regarding the effectiveness of Health Canada's drug approval process in 1996.It highlights the resulting impact on the whistleblower, the organization, and ultimately the implications for public safety and accountable government.The methods used were process-tracing, in-depth interviews and data and document review.The results suggested problems with culture in the main organization Health Canada, possibly exacerbated by deregulation.The conclusion is a multi-faceted approach to addressing culture is needed before whistleblower protection legislation can work and accountable organizations can flourish.iii who was second reader.His astute comments and questions kept me focused, and at the same time stimulated me to think more broadly -not an easy feat.Thanks also to the other members of my Defence Committee, external member Assoc.
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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 | 0.018 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.042 | 0.026 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".