African climate change policy performance index
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it