Confronting Complex Accountability in Conservation With Communities
Bibliographic record
Abstract
Increasingly, conservation organizations are conducting conservation activities with local communities. Many conservation organizations now position their work as contributing to sustainable development initiatives, and local involvement in conservation is understood to increase conservation and sustainability success. Aside from communities, however, conservation organizations are accountable to funders and partners, and values and priorities vary across actor type. Mismatched goals combine with power imbalances between conservation actors, and create decision-making conflict throughout conservation processes, from objective setting through implementation and evaluation. As a result, communities may lose local decision-making power or face new negative consequences, trust in organizational/community partnerships may be undermined, and conservation organizations’ reputations (and the reputation of the sector as whole) may suffer. In this commentary we point out processes and conditions that can lead conservation organizations to privilege accountability to funders and others over accountability to communities, thereby undermining community-level success. We follow with suggestions for how funders, conservation organizations and others may improve community engagement and community-level outcomes, and improve their reputations in general and in their work with communities, by actively leveraging accountability to the community and involving local community members in decision-making.
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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 | 0.096 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.029 | 0.090 |
| Scholarly communication | 0.026 | 0.031 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.020 | 0.023 |
| Insufficient payload (model declined to judge) | 0.005 | 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".