Influence of funding fads and donor interests on international aid for conservation in Madagascar
Bibliographic record
Abstract
Tens of billions of dollars in official development assistance have been spent over the past three decades to address the increasingly rapid loss of biodiversity globally. Despite this expenditure, detailed knowledge of who has provided these funds and who has used them, for what purpose, where, why, and with what consequences remains limited. To address this gap, we used a mixed-methods approach to map and analyze international aid for biodiversity conservation in Madagascar, a high-priority country for conservation. We combined collation and analysis of publicly available funding data with semistructured interviews with a range of conservation actors in Madagascar. Overall, biodiversity aid to the country declined from 1990 to 2018 and was punctuated by sharp declines during times of political unrest. Funding flows were marked by periods with distinctive emphases, from institutional development to protected areas, to creating market-based incentives for conservation. These patterns reflected key donor interests and resonated with the views and perceptions of conservation practitioners on the ground. Conservation professionals highlighted how administrative shortsightedness and imbalances in the power relations shaping conservation aid allocation have led to an increasing projectification of the conservation sector and weakening of state capacity. Our findings show that by studying how funding for biodiversity changes within countries over time, one can reveal the interests and power dynamics among donors, governments, and nongovernmental organizations that influence funding decisions and conservation efforts. The evidence and insights presented here can inform future biodiversity funding decision-making in Madagascar and elsewhere and have particular relevance given major funding commitments under the Kunming-Montreal Global Biodiversity Framework.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".