When Agendas Collide: Human Welfare and Biological Conservation
Bibliographic record
Abstract
Conservation should benefit ecosystems, nonhuman organisms, and current and future human beings. Nevertheless, tension among these goals engenders potential ethical conflicts: conservationists' true motivations may differ from the justifications they offer for their activities, and conservation projects have the potential to disempower and oppress people. We reviewed the promise and deficiencies of integrating social, economic, and biological concerns into conservation, focusing on research in ecosystem services and efforts in community-based conservation. Despite much progress, neither paradigm provides a silver bullet for conservation's most pressing problems, and both require additional thought and modification to become maximally effective. We conclude that the following strategies are needed to make conservation more effective in our human-dominated world. (1) Conservation research needs to integrate with social scholarship in a more sophisticated manner. (2) Conservation must be informed by a detailed understanding of the spatial, temporal, and social distributions of costs and benefits of conservation efforts. Strategies should reflect this understanding, particularly by equitably distributing conservation's costs. (3) We must better acknowledge the social concerns that accompany biodiversity conservation; accordingly, sometimes we must argue for conservation for biodiversity's sake, not for its direct human benefits.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".