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 Global politics has shown increasing interest in cities, particularly in the field of climate policy and governance. Yet, we still have little understanding of which cities engage the most in global urban climate governance. Answering this question is a first step towards understanding who decides for whom in a system that has decisive influence on wider global policy processes. In this article, we seek to identify and analyse the characteristics and position of cities in global urban climate governance to reassess its composition. To do so, we conduct a social network analysis of 15 transnational city networks. Results emphasise that global and large cities are the most central, but small and middle‐size cities are the most numerous actors of the system. Global South cities are larger than their Northern counterparts in the system. Those less central and understudied actors likely have less influence over which norms are shared, yet they should not be seen as followers or imitators of climate policy. It is important to pay more attention to them to understand their multifaceted role in cities' collective efforts to address climate change.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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