Global cities could shape new climate agenda
Bibliographic record
Abstract
Subject The role of cities in climate governance. Significance Last week the US State Department and UN Special Envoy for Climate Change and Cities Michael Bloomberg held a special summit of mayors from major global cities to share best practices on addressing climate change. Given past roadblocks stymieing inter-state climate negotiations, policymakers are pinning hopes on cities to sustain momentum ahead of the UN climate change summit (COP21) in December. Impacts Limited fiscal resources will push cities to use their existing assets more efficiently, including through 'big data' approaches. Philanthropic initiatives, eg by the Rockefeller and Bloomberg foundations, will help spread climate mitigation and adaptation expertise. Despite being disproportionately affected by climate change, African cities' limited policy powers will constrain their adaptation. Strong regional identities will drive some subnational diplomatic efforts, notably those by Catalonian and Quebecois authorities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.111 | 0.020 |
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".