Green Attraction—Transnational Municipal Climate Networks and Green City Branding
Bibliographic record
Abstract
In this article, we investigate the nexus of green city branding and municipal climate networks. In recent decades, a number of formal transnational municipal climate networks have emerged and their membership continues to increase. In parallel, city branding that is based on green policies, has gained importance. Based on quantitative and qualitative data, we assess how and to what extent German cities use their membership in transnational municipal climate networks to communicate green city brands. In contrast to our expectations, we encountered very few indications of green city branding efforts by German cities. Our analysis shows that in general, branding considerations only play a negligible role in the involvement of cities in transnational municipal climate networks or climate policies. Instead, it seems that German cities use their membership in climate networks, to genuinely improve local climate change strategies. We therefore suggest that research on green city branding should be more sensitive to the particular context of cities and efforts should be made to unveil the underlying motives for the communication of green policies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".