Striking a balance: A proposed benefit assessment matrix for ethical animal research
Bibliographic record
Abstract
The debate about the continued use of animals in research has intensified in recent years, with few signs that consensus can be achieved in the near future. Animal activist groups and their supporters call for an immediate halt to all animal research. The counterargument from research scientists and many others across various facets of society is that suitable alternatives have not been sufficiently established and validated to enable all animal research to cease without halting scientific progress. We suggest a compromise rooted in the existing regulations and based on the consensus that not all research efforts contribute enough benefit to ethically justify the use of animals in research. More specifically, we describe a Benefit Assessment Matrix that can provide a streamlined and practical guide for both scientists and Animal Ethics Committees or equivalent bodies such as Institutional Animal Care and Use Committees to assess the benefit of proposed research. The organizational premise is that rigorous high-quality research is more likely to produce tangible benefit than poor quality, low rigor research. Implementation of the Benefit Assessment Matrix will enable more rapid phasing out of poor-quality research and support the objective of promoting rigorous high-quality research, meeting expectations of both sides of the debate.
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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 | 0.216 | 0.204 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.032 | 0.024 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".