Structuring Public Engagement for Effective Input in Policy Development on Human Tissue Biobanking
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
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.
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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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