Public consultation in the evaluation of animal research protocols
Bibliographic record
Abstract
One response to calls for increased openness in animal research is to make protocols publicly accessible, but it is unclear what type of input the public would provide if given this opportunity. In this study we invited public responses to five different research projects, using non-technical summaries intended for lay audiences. Our aim was to assess the potential for this type of public consultation in protocol review, and a secondary aim was to better understand what types of animal research people are willing to accept and why. US participants (n = 1521) were asked (via an online survey) "Do you support the use of these (insert species) for this research", and responded using a seven-point scale (1 = "No", 4 = "Neutral", and 7 = "Yes"). Participants were asked to explain the reasons for their choice; open-ended text responses were subjected to thematic analysis. Most participants (89.7%) provided clear comments, showing the potential of an online forum to elicit feedback. Four themes were prevalent in participant reasoning regarding their support for the proposed research: 1) impact on animals, 2) impact on humans, 3) scientific merit, and 4) availability of alternatives. Participant support for the proposed research varied but on average was close to neutral (mean ± SD: 4.5 ± 2.19) suggesting some ambivalence to this animal use. The protocol describing Parkinson's research (on monkeys) was least supported (3.9 ± 2.17) and the transplant research (on pigs) was most supported (4.9 ± 2.02). These results indicate that public participants are sensitive to specifics of a protocol. We conclude that an online forum can provide meaningful public input on proposed animal research, offering research institutions the opportunity for improved transparency and the chance to reduce the risk that they engage in studies that are out of step with community values.
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
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 | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".