Ethical issues faced by field primatologists: asking the relevant questions
Bibliographic record
Abstract
Field primatologists face unusual ethical issues. We study animals rather than people and receive research approval from animal care rather than ethics committees. However, animal care evaluation forms are developed from concerns about laboratory animal research and are based on the "Three R's" for humane treatment of captive experimental subjects (replacement, reduction and refinement), which are only debatably relevant to field research. Scientists who study wild, free-ranging primates in host countries experience many ethical dilemmas seldom dealt with in animal care forms. This paper reviews the ethical issues many field primatologists say they face and how these might be better addressed by animal care forms. The ethical issues arising for field researchers are divided into three categories: "Presence, Protocols and People" and for each the most frequent issues are described. The most commonly mentioned ethical concern arising from our presence in the field is the possibility of disease transmission. Although most primate field studies employ only observational protocols, the practice of habituating our study animals to close human presence is an ethical concern for many since it can lessen the animals' fear of all humans, thereby facilitating undesirable behaviors (e.g., crop-raiding) and rendering them vulnerable to harm. Field primatologists who work in host countries must observe national laws and local traditions. As conservationists, primatologists must often negotiate between the resource needs and cultural practices of local people and the interests of the nonhuman primates. Many say they face more ethical dilemmas arising from human interactions than from research on the animals per se. This review concludes with suggestions for relevant questions to ask on animal care forms, and actions that field primatologists can take to better inform animal care committees about the common ethical issues we experience as well as how to develop guidelines for addressing them.
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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 | 0.123 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.034 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.033 | 0.036 |
| Insufficient payload (model declined to judge) | 0.003 | 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".