Animals in Science: Ethical Justifications, Regulatory Frameworks, and Political Recommendations in the Canadian Context
Bibliographic record
Abstract
Global estimates suggest that more than 100 million non-human animals are used for scientific purposes each year. The nature of the research, teaching, and testing conducted on these animals can be very invasive, painful, and fatal. Should we care? To discontinue these practices in some cases may result in human suffering. Should any human benefits of research, teaching, and testing outweigh the resultant animal suffering? This paper begins with an analysis of some of the most popular theories on the moral status of animals. From this analysis it is argued that mere species membership is not a morally relevant characteristic, and that non-human animals can have moral status and moral rights. A deontological approach to adjudicating moral claims across species is presented to overcome some of the challenges typical of utilitarian and rights-based approaches. This approach is used to sketch a general framework for evaluating which types of scientific animal use ought to be permitted. It is argued further that, while some forms of scientific animal use may be permitted at present, we ought to strive for the elimination of the practice. The focus will then shift to an analysis of Canada’s regulatory system for the scientific use of animals, identifying shortcomings of this system. The Canadian approach to regulation in this area will be compared against approaches that are taken in the UK and the Netherlands which are more closely aligned with the moral arguments made in the first section. There are opportunities for Canada to learn from these countries, and remarks will be made on how and why Canada should improve the regulation of animals in research, teaching, and testing. Such changes have the potential to improve the wellbeing not only of the animals used in science, but for humans as well. Finally, expected costs and benefits that would accompany the implementation of the recommendations are considered with comments on how costs can be alleviated and why they should be incurred.
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
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 | 0.028 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.040 | 0.050 |
| Scholarly communication | 0.025 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.015 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".