Who’s setting the agenda? Philanthropic donor influence in marine conservation
Bibliographic record
Abstract
We are in a period of unprecedented growth in conservation philanthropy. How will this influx of private funding affect conservation agendas? Inspired by a collaborative research co-design process, this paper addresses questions about how foundations influence conservation agendas in the places they work. We draw from a case study of the world’s largest philanthropic funder of marine conservation, the David and Lucile Packard Foundation and their 20 years of investment in marine conservation in Palau and Fiji. Conservation practitioners in both countries universally agreed that the Packard Foundation had a significant and positive influence on the agenda, which they attribute to both how the foundation worked and what they chose to fund. Specifically, our study reveals how the Packard Foundation shaped conservation agendas in Palau and Fiji in partnership with its grantees through a grant-making process characterized by relationship building, collaborative decision making, convening and promoting of collective action, flexibility, and long-term funding. Packard’s approach was often identified as unique, and contrasted with numerous other donors, including foundations and other types of donors, who use a more top-down approach. By describing a relative success story in how philanthropic foundations can work with conservation practitioners to co-design a shared conservation agenda, our work provides timely guidance for donors and practitioners.
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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 | 0.039 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| 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".