Bibliographic record
Abstract
Highlights With their predominantly coastal geographies, rapidly growing populations, and emissions-intensive activities, cities are highly vulnerable and major contributors to climate change. Their role as cultural centers, and commerce and innovation hubs, means they are also promising sources of solutions. Taken together, these factors demand a closer examination of the progress and solutions that cities are making to mitigate climate change and adapt to its impacts. However, research on the extent and effectiveness of cities’ implementation efforts is underdeveloped. There is a need to better understand if and how cities are rolling out effective implementation measures, what effects (intended and unintended) such measures are having, and whether their implementation efforts are achieving the transformational changes needed to realize a low carbon, climate-resilient future. This editorial introduces the special issue by exploring these issues and reflecting perspectives from a variety of disciplines both within and outside academia, and in relation to diverse cities in the Global North and South. To better understand the practical dimensions of implementation, and the various obstacles and opportunities faced by public and private sector actors in progressing climate action targets and goals, the editors invited submissions reflective of co-produced research. Though not all took this form, some did and helped to foreground the experiences of those actors who arguably have the most power and responsibility to advance implementation measures, and seed the very institutional arrangements needed for deeper, multisectoral climate action. Collectively, the content of the special issue points to a need for significant investment, policy change, social innovation, and cooperation across societal scales.
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.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 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".