Advancing Ethical Principles for Non-Invasive, Respectful Research with Nonhuman Animal Participants
Bibliographic record
Abstract
Abstract Animal studies scholars are increasingly engaging with nonhuman animals firsthand to better understand their lifeworlds and interests. The current 3R framework is inadequate to guide respectful, non-invasive research relations that aim to encounter animals as meaningful participants and safeguard their well-being. This article responds to this gap by advancing ethical principles for research with animals guided by respect, justice, and reflexivity. It centers around three core principles: non-maleficence (including duties around vulnerability and confidentiality); beneficence (including duties around reciprocity and representation); and voluntary participation (involving mediated informed consent and ongoing embodied assent). We discuss three areas (inducements, privacy, and refusing research) that merit further consideration. The principles we advance serve as a starting point for further discussions as researchers across disciplines strive to conduct multispecies research that is guided by respect for otherness, geared to ensuring animals’ flourishing, and committed to a nonviolent ethic.
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 | 0.322 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.083 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.019 | 0.024 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".