Green Urban (RE) Generation: A Research and Practice Methodology to Better Implement Green Urban Infrastructure Solutions
Bibliographic record
Abstract
Green Urban Infrastructure Solutions (GUIS) are becoming more and more popular globally. Recent research reveals the environmental benefits derived from GUIS as well as their contribution to climate change adaptation. However, the urgent need for GUIS in order to meet the Paris Agreement, has not translated into an easy implementation thereof. This paper proposes a circular design methodology (CDM) where the combination of research and practice contributes to minimize both current skepticism and barriers when implementing GUIS. It includes a community engagement process to better understand their sensitivity and build consensus on GUIS. Additionally, GUIS are implemented, in a series of pilot projects and specific research is applied to comprehend the environmental benefits derived from these GUIS. The paper argues that GUIS represent a significant opportunity to respond to climate change risks as well as to achieve other urban benefits; however, in order to overcome existing barriers and skepticism, the proposed CDM reaches for more consensual urban solutions and drives uptake and implementation of GUIS, contributing to move from pilot project to common practice.
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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 | 0.104 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".