Engaging the Public on Biobanks: Outcomes of the BC Biobank Deliberation
Bibliographic record
Abstract
In April 2007, a research team led by M. Burgess conducted a public engagement, the BC Biobank Deliberation, focused on the issue of biobanks. The project was motivated by an observation that current policy approaches to social and ethical issues surrounding biobanks manifest certain democratic deficits. The public engagement was informed by political theory on deliberative democracy with the aim of informing biobanking policies, in particular in British Columbia (BC), Canada. The purpose of this paper is to provide a comprehensive outline of the conclusions reached by the deliberants (both recommendations based on consensus and issues that emerged as persistent disagreements). However, the process whereby the specific conclusions to be delivered to policy makers are identified is not a self-evident process. We thus provide a critical analysis of how the results of a public engagement such as the BC Biobank Deliberation can be conceptualized given the context of a large qualitative data set and an imperative to provide useful information to policy makers, while honoring the mandate under which deliberants were recruited. In particular, we make the case for distinguishing between deliberative outputs of public engagement and analytical outputs that are the product of social scientific analyses of such engagements.
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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 | 0.065 | 0.178 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.018 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.005 | 0.009 |
| 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".