The City Research and Innovation Agenda: Prioritizing Knowledge Gaps and Policy Processes to Accelerate City Climate Action
Bibliographic record
Abstract
We present city specific climate action policy recommendations for urban practitioners, government officers, city leaders, and research decision-makers, from the recently released City Research and Innovation Agenda (CRIA). Building on the Global Research Action Agenda for Cities Climate Change Science 2018, updated in 2021—key outputs from the first Cities and Climate Change Science Conference in Edmonton 2018 and the follow up Innovate4Cities 2021 Conference (online)—the CRIA presents the priority city research and policy gaps identified at these two major conferences. With a focus on the nexus of cities, climate change science, and policy innovation, the CRIA is for policy and decision-makers looking to make significant progress on city focused climate action strategies and plans, by focusing their attention on the city-relevant evidence and outcome-oriented partnerships needed across academia, government, business, and civil society. Here, we provide a summary of the CRIA, that presents priorities under four key questions that cities and their partners in research and innovation ask as they develop and implement climate plans: How do we build the evidence base for climate action? How and for whom should we prioritize? What should we do? and How do we finance and scale climate action? We include arguments for why consolidated understanding of data, technology, and knowledge gaps across a city climate action journey is important to accelerate implementation of cities’ climate commitments, as is the need for key partnerships to support cities in meeting climate goals.
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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 | 0.279 | 0.257 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.020 | 0.023 |
| Scholarly communication | 0.063 | 0.057 |
| Open science | 0.016 | 0.070 |
| Research integrity | 0.064 | 0.055 |
| Insufficient payload (model declined to judge) | 0.037 | 0.013 |
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".