Structuring Public Engagement for Effective Input in Policy Development on Human Tissue Biobanking
Bibliographic record
Abstract
We begin with the premise that human tissue biobanking is associated with ethical ambiguities and regulatory uncertainty, and that public engagement is at least one important element in addressing such challenges. One is then confronted with how to achieve public engagement that is both meaningful and effective. In particular, how can public engagement on the topic of biobanking be implemented so that (a) it is perceived broadly as legitimate and (b) the results of the engagement are relevant and useful to the institutional and regulatory context? In this paper we build on previous work that has addressed the former point and focus primarily on the latter. We argue that one way to increase the likelihood of results of public engagement being taken up in policy is through framing the issues that are deliberated by members of the public based in part on the practical policy questions for which input is sought. In this approach, we move discussion on the social and ethical implications of biobanking from abstract principles, to their consideration in the context of local biobanking practices. This is illustrated using a practical example involving a public engagement conducted to inform institutional policy for biobanking in British Columbia, Canada.
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.
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 | 0.302 | 0.258 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.022 | 0.048 |
| Scholarly communication | 0.045 | 0.034 |
| Open science | 0.007 | 0.040 |
| Research integrity | 0.041 | 0.035 |
| Insufficient payload (model declined to judge) | 0.014 | 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".