African climate change policy performance index
Bibliographic record
Abstract
The African Climate Change Policy Performance Index (ACCPPI) evaluates and assesses countries and regions in Africa in terms of their climate change policy performance. The ranking is based on four key scores which are: the greenhouse emissions score (30%), the renewable energy score (25%), the climate policy score (25%), and the corruption perception score (20%). This index fills a major research gap in the context of climate change policy performance. This index is the first index that provides a comprehensive outlook on the state of climate change policy performance in Africa. The initial results from a country perspective show that Morocco, Cape Verde, Angola, Senegal, Ghana, Tanzania, and Zambia are the best performers. Regionally, North and Southern Africa are the best performers. This index provides and outlook of what is happening across Africa and where stakeholders must make more efforts. The ACCPPI will move the climate change policy performance debate in Africa from emotional and rhetorical evaluations to more data and evidence-based actions that facilitates climate change policy performance tracking and accounting. The tool is a first of its kind and will be a standard bearer for comparing and tracking climate change policy performance across Africa. It will be updated every five years to introduce new data and track new developments while influencing climate change policy across Africa.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".