A club convergence analysis of climate change from a cross-country perspective
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
Abstract Does evidence exist of convergence clubs in global climate change vulnerability/readiness? This question is pivotal as it dictates the necessity for policy collaboration in addressing climate change. This study investigates the climate change convergence hypothesis utilizing climate vulnerability index data from 136 countries spanning 1995–2020. Employing club convergence methodology, which clusters countries with similar characteristics while accommodating country heterogeneity, the analysis reveals an initial classification comprising 14 clubs. The study identifies that the first four clubs consist of low- and medium-low-income countries in Africa and South Asia, while the last four clubs comprise high-income countries situated among the USA, Canada, and Europe. These findings show high, low- and middle-income countries' responses to climate change.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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