Mad scientists bend the frame of biobank governance in British Columbia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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’.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it