Fostering the ‘Time is Now' Mentality: The Role of Open Data In Urban Climate Resilience
Bibliographic record
Abstract
Climate change is a systemic issue embedded in and interconnected with the social and economic makeup of a city. Building urban climate resilience requires innovative, collaborative solutions that hinge upon the openness and availability of current and contextual data. Open data tools, in stimulating information sharing, civic engagement, and innovative products, can contribute to climate change planning, building lasting resilience. Through an exploratory research methodology, this paper explores 17 international use cases, providing a basis for the implementation of open data tools in the realm of urban climate resilience, through the following five themes: 1) risk and vulnerability assessment; 2) the inception of initiatives; 3) diverging approaches to preparedness; 4) community mobilization; and 5) mitigation and adaptation. This research aims to spark a dialogue on the intersection of open data tools in urban climate resilience strategies, demonstrating open data as an appropriate tool to cultivate shared understanding and collective action.
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How this classification was reachedexpand
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 | 0.004 | 0.040 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".