Measuring and tracking the flow of climate change adaptation aid to the developing world
Bibliographic record
Abstract
The developed world has pledged to mobilize at least US $100 billion per year of 'new' and 'additional' funds by 2020 to help the developing world respond to climate change. Tracking this finance is particularly problematic for climate change adaptation, as there is no clear definition of what separates adaptation aid from standard development aid. Here we use a historical database of overseas development assistance projects to test the effect of different accounting assumptions on the delivery of adaptation finance to the developing countries of Oceania, using machine algorithms developed from a manual pilot study. The results show that explicit adaptation finance grew to 3%–4% of all development aid to Oceania by the 2008–2012 period, but that total adaptation finance could be as high as 37% of all aid, depending on potentially politically motivated assumptions about what counts as adaptation. There was also an uneven distribution of adaptation aid between countries facing similar challenges like Kiribati, the Marshall Islands, and the Federated States of Micronesia. The analysis indicates that data allowing individual projects to be weighted by their climate change relevance is needed. A robust and mandatory metadata system for all aid projects would allow multilateral aid agencies and independent third parties to perform their own analyses using different assumptions and definitions, and serve as a key check on international climate aid promises.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".