Challenging Structural Barriers to Creating Ethical Space in Wildlife Research Ethics Policy
Bibliographic record
Abstract
Canadian institutions face public and political pressure to include Indigenous voices and Indigenous Knowledge in their governing structures. This often results in a scramble to retrofit boards and committees to demonstrate inclusivity, often by inviting Indigenous representatives or knowledge from Indigenous sources. However, representation does not mean attributing equal power to those representatives if institutions do not challenge their inner policy structures. In research spaces, this can be observed in research ethics policy, which has been widely challenged but remains stubborn to change. This report seeks to identify the procedures and policies within animal care and ethics that categorically exclude knowledge and knowledge-holders falling outside the Western Scientific Knowledge realm. We explore this subject by identifying exclusive policies and procedures within an exemplar ethics board, an Animal Care Committee under the guidelines of the Canadian Council on Animal Care (CCAC). We briefly identify some of the foundational beliefs underlying animal care and ethics policy, how they inform current policy, and how they are exclusive of knowledge outside the Western Scientific worldview. We urge ACC committees at institutions to revisit these policies and to adapt their vision on knowledge, credibility, respectful protocols, and acceptable justification for research, for which we provide several recommendations.
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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 | 0.217 | 0.171 |
| Meta-epidemiology (narrow) | 0.000 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.039 | 0.092 |
| Scholarly communication | 0.034 | 0.014 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.004 | 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".