Consequences Matter: Compassion in Conservation Means Caring for Individuals, Populations and Species
Bibliographic record
Abstract
Human activity affecting the welfare of wild vertebrates, widely accepted to be sentient, and therefore deserving of moral concern, is widespread. A variety of motives lead to the killing of individual wild animals. These include to provide food, to protect stock and other human interests, and also for sport. The acceptability of such killing is widely believed to vary with the motive and method. Individual vertebrates are also killed by conservationists. Whether securing conservation goals is an adequate reason for such killing has recently been challenged. Conventional conservation practice has tended to prioritise ecological collectives, such as populations and species, when their interests conflict with those of individuals. Supporters of the 'Compassionate Conservation' movement argue both that conservationists have neglected animal welfare when such conflicts arise and that no killing for conservation is justified. We counter that conservationists increasingly seek to adhere to high standards of welfare, and that the extreme position advocated by some supporters of 'Compassionate Conservation', rooted in virtue ethics, would, if widely accepted, lead to considerable negative effects for conservation. Conservation practice cannot afford to neglect consequences. Moreover, the do-no-harm maxim does not always lead to better outcomes for animal welfare.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.031 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".