Holding Animal-Based Research to Our Highest Ethical Standards: Re-seeing Two Emergent Laboratory Practices and the Ethical Significance of Research Animal Dissent
Bibliographic record
Abstract
"Animal-based research should be held to the highest ethical standards" is becoming an increasingly common refrain. Though I think such a commitment is what we should expect of those using animals in science, much as we would if the participants were humans, some key insights of discussions in applied ethics and moral philosophy only seem to slowly impact what reasonably qualifies as the highest standards in animal research ethics. Early in my paper, I will explain some of these insights and loosely tie them to animal research ethics. Two emergent practices in laboratory animal science, positive reinforcement training and "rehoming," will then be discussed, and I will defend the view that both should be mandatory on no more ethical grounds than what is outlined in the first section. I will also provide reasons for foregrounding the moral significance of dissent and why, most of the time, an animal research subject's sustained dissent should be respected. Taken together, what I will defend promises to change how at least some animals are used in science and what happens to them afterwards. But I will also show how an objective ethics requires nothing less. Ignoring these constraints in the scientific use of animals comes at the cost of abandoning any claim to adhering to our highest ethical standards and, arguably, any claim to the moral legitimacy of such scientific use.
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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 | 0.125 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.155 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.011 | 0.033 |
| Insufficient payload (model declined to judge) | 0.001 | 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, 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".