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Record W4411311596 · doi:10.1007/s10806-025-09951-7

Challenging Structural Barriers to Creating Ethical Space in Wildlife Research Ethics Policy

2025· article· en· W4411311596 on OpenAlexaffabout
Élise Brown-Dussault, Jared Gonet, Tara E. Stehelin

Bibliographic record

VenueJournal of Agricultural and Environmental Ethics · 2025
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsYukon Department of EnvironmentYukon University
Fundersnot available
KeywordsWildlifeSpace (punctuation)Engineering ethicsEnvironmental ethicsSociologySpace policyEthical issuesPolitical scienceEngineeringComputer scienceEcologyPhilosophyBiology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.516
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.007
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.395
Teacher spread0.348 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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