Compassionate conservation practice: supporting diverse conservation actions but context matters
Bibliographic record
Abstract
Compassionate conservation advocates for an approach that considers the wellbeing of non-human animals and the protection of nature as interconnected and mutually supportive. Critics argue that compassionate conservation is an ineffective, ‘do-nothing’ approach that poses significant threats to biodiversity, people, wildlife, and ecosystems. These criticisms often stem from the assumption that compassionate conservationists oppose any kind of conservation action, which may result from a lack of clarity about which actions are consistent with this approach. To address this, we employed a Delphi method to engage a community of 27 compassionate conservationists in a structured discussion about 100 actions, across 16 domains of practice. Participants indicated whether these actions aligned with compassionate conservation. Results showed that participants supported a diversity of conservation actions. Specifically, 35 actions received high levels of support (≥80 % positive response), 19 received low levels of support (≤20 % positive response), and 46 received variable levels of support (between 20 % and 80 % positive responses). Through a thematic analysis of text-based participant responses, we identified five major themes and 18 subthemes that illustrate the contextual factors influencing participants' support of actions. For compassionate conservationists, effective conservation action involves prioritizing animal wellbeing, fostering the flourishing of ecological and social communities, and carefully assessing the potential for harm. They emphasize the importance of assessing context and contingency, recognizing that no single consideration is solely predictive, causative, or morally justifiable.
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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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".