Recruiting for representation in public deliberation on the ethics of biobanks
Bibliographic record
Abstract
This paper addresses the dilemmas of participant sampling and recruitment for deliberative science policy projects. Results are drawn from a deliberative public event that was held in April and May, 2007. The research objective of The BC Biobank Deliberation was to assess deliberative democracy as an approach to legitimate policy advice from a subset of British Columbians concerning the secondary use of human tissues for prospective genomic and genetic research. The overall goal was to have participants identify key values that should guide a biobank in British Columbia. This paper assesses our team's group decision-making processes concerning participant sampling for the 2007 event. Results presented here should allow the reader to critically examine our team's choices and could also be used to assist advocates of deliberative democracy and others who may wish to propose similar events in the future.
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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 | 0.570 | 0.486 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.030 | 0.040 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.004 | 0.033 |
| Research integrity | 0.012 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".