Community engagement and power dynamics in conservation philanthropy grant making
Bibliographic record
Abstract
Funding decisions influence where, how, and by whom conservation is pursued globally. In the context of growing calls for more participatory, Indigenous-led, and socially just conservation, we undertook the first empirical investigation of how philanthropic foundations working in marine conservation globally engage communities in grant-making decisions. We paid particular attention to whether and how community engagement practices reinforce or disrupt existing power dynamics. We conducted semistructured remote interviews with 46 individuals from 32 marine conservation foundations to identify how conservation foundations engage communities in setting their priorities and deciding which organizations and projects to fund. We found that community engagement in foundation decision-making was limited in practice. Eleven of the 32 foundations reported some form of community engagement in funding decisions. Two of these foundations empowered communities to shape funding priorities and projects through strong forms of engagement. Many engagement practices were one way, one time, or indirect and confined to certain points in decision-making processes. These weaker practices limited community input and reinforced unequal power relations, which may undermine the legitimacy, equity, and effectiveness of conservation efforts. We suggest that foundations aim for stronger forms of community engagement and reflect on how their grant-making practices affect power relations between foundations and communities.
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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 | 0.028 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".