The Political Dimension of Vulnerability: Implications for the Green Climate Fund
Bibliographic record
Abstract
As the availability of adaptation finance for developing countries increases, so does the need for a transparent way of prioritising countries for the allocation of money. It is intuitive that some countries are more vulnerable to climate change than others, and that countries that are particularly vulnerable should be given priority for adaptation finance. However, research has shown that science cannot be relied upon for a single objective ranking of vulnerability. This article analyses how the Global Climate Change Alliance (GCCA), the Pilot Program for Climate Resilience (PPCR) and the Adaptation Fund currently make decisions on adaptation finance allocations. It finds that each of the funds uses vulnerability to prioritise among countries, but the criteria applied vary and other criteria also play a role. Thus, vulnerability is politically, as well as scientifically, ambiguous. The Cancun Agreements have not resolved this, leaving a challenge for the Green Climate Fund.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| 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".