Public health, biopower and whistleblowing : The case of a leaked Covid-19 report in British Columbia (BC)
Bibliographic record
Abstract
Rapidly changing populations, new technologies and outbreaks of diseases on a global scale have made the need for Public health organisations to adapt rapidly. It is not surprising then, that in these conditions Public Health officials have had to come up with new strategies and change processes in order to organise effectively and continue providing services to the public. This thesis deals with what happens when during such a change process that requires rapid action, public health organizations are confronted with whistleblowers or leaking behaviour. Utilising the concepts of biopolitics, biopower and algorithmic governmentality, the empirical example of BC’s leaked covid report is examined to understand the impact of leaking on public health and its change processes. Viewing change as a game of power that goes beyond simply classifying change as good or bad, but rather taking into consideration context, power dynamics and shifting perspectives with an ever evolving pandemic looming over public health organisations as they have to make decisions that impact whole countries. Leaking behaviour becomes the spotlight as it highlights exactly how certain decisions taken by public health as part of the rapid change processes may be misconstrued or misunderstood. Therefore highlighting the power that whistleblowers and leakers can hold over big corporations and how they may or may not lead to organisational change
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.013 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| 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; 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".