The ethical dimensions of wildlife disease management in an evolutionary context
Bibliographic record
Abstract
Best practices in wildlife disease management require robust evolutionary ecological research (EER). This means not only basing management decisions on evolutionarily sound reasoning, but also conducting management in a way that actively contributes to the on-going development of that research. Because good management requires good science, and good science is 'good' science (i.e., effective science is often science conducted ethically), good management therefore also requires practices that accord with sound ethical reasoning. To that end, we propose a two-part framework to assist decision makers to identify ethical pitfalls of wildlife disease management. The first part consists of six values - freedom, fairness, well-being, replacement, reduction, and refinement; these values, developed for the ethical evaluation of EER practices, are also well suited for evaluating the ethics of wildlife disease management. The second part consists of a decision tree to help identify the ethically salient dimensions of wildlife disease management and to guide managers toward ethically responsible practices in complex situations. While ethical reasoning cannot be used to deduce from first principles what practices should be undertaken in every given set of circumstances, it can establish parameters that bound what sorts of practices will be acceptable or unacceptable in certain types of scenarios.
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 | 0.045 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".