Equity trade‐offs in conservation decision making
Bibliographic record
Abstract
Conservation decisions increasingly involve multiple environmental and social objectives, which result in complex decision contexts with high potential for trade-offs. Improving social equity is one such objective that is often considered an enabler of successful outcomes and a virtuous ideal in itself. Despite its idealized importance in conservation policy, social equity is often highly simplified or ill-defined and is applied uncritically. What constitutes equitable outcomes and processes is highly normative and subject to ethical deliberation. Different ethical frameworks may lead to different conceptions of equity through alternative perspectives of what is good or right. This can lead to different and potentially conflicting equity objectives in practice. We promote a more transparent, nuanced, and pluralistic conceptualization of equity in conservation decision making that particularly recognizes where multidimensional equity objectives may conflict. To help identify and mitigate ethical conflicts and avoid cases of good intentions producing bad outcomes, we encourage a more analytical incorporation of equity into conservation decision making particularly during mechanistic integration of equity objectives. We recommend that in conservation planning motivations and objectives for equity be made explicit within the problem context, methods used to incorporate equity objectives be applied with respect to stated objectives, and, should objectives dictate, evaluation of equity outcomes and adaptation of strategies be employed during policy implementation.
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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 | 0.055 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.029 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".