Mad scientists bend the frame of biobank governance in British Columbia
Bibliographic record
Abstract
The tools and rhetoric of deliberative democracy are increasingly popular with governments, organizations, and researchers working to enhance ‘public engagement with science’. Deliberative fora such as citizen juries have also been heavily critiqued by social and political scientists – for positively and narrowly framing contentious new technologies to secure public support, and for privileging consensus over ‘difference’. This paper takes such critiques seriously. Drawing from ethnographic participant-observation and analysis of a deliberative public consultation on biobanking in British Columbia (BC), Canada, it argues for careful attention to deliberative event design. A multi-disciplinary approach, multiple media, and imagination-focused tasks were used in BC to produce inclusive deliberations in which members of the public were able to directly challenge expert assumptions. Ethnographic attention to narrative during analysis of the deliberation reveals the extent to which participants insistently questioned the framing of the event. Drawing from personal experiences, analogies, news stories and fictional events, the deliberants developed and embellished the figure of a ‘mad scientist’ to challenge certainties promised by scientific, legal, and ethical expertise. This paper argues that such questioning enhanced the accountability of the deliberation and participant trust in the event. It also argues that ethnographic attention to storytelling is a valuable and under-utilized pursuit in the field of deliberative democracy – a pursuit that can enable deliberative events to ‘listen’.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.016 |
| Scholarly communication | 0.013 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".