Engaging the public in the regulation of xenotransplantation: would the Canadian model of public consultation be effective in the US?
Bibliographic record
Abstract
The value of engaging the public in science policymaking is becoming increasingly controversial. In the US, however, the regulation of biotechnologies remains in the domain of the scientific elite. By contrast, Canada recently conducted a very public process aimed at including the public in the regulation of xenotransplantation. Members of the US xenotransplantation community were asked to comment on the public consultation process with specific attention given to the Canadian consultation on xenotransplantation. These scientists agreed that gathering public opinion is usually desirable but expressed some serious concerns about the methods used to gather these opinions. They challenged the notion of an informed public as defined by the organizers of the Canadian consultation. Therefore, in order for the Canadian model of public consultation to be well received in the US, there would have to be more stringent adherence to representative sampling and more rigorous public education strategies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Qualitative | high |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | high |
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 | 0.054 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.031 | 0.037 |
| Scholarly communication | 0.020 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.021 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".