GREEN TRAJECTORIES municipal policy trends and strategies for greening in Europe, Canada and United States (1990-2016)
Bibliographic record
Abstract
This book examines the urban greening policy trajectories of 50 cities in Europe, Canada and the United States over the last 25 years. It identifies the main trends and strategies used and ranks cities along key criteria including the level of rhetoric, focus on health, and equitable access. The book is the result of the first stage of the GreenLULUs study, a 5-year research project examining the relationship between urban greening and social equity funded by the European Research Council and undertaken by the Barcelona Lab for Urban Environmental Justice and Sustainability (BCNUEJ) at the Institute of Environmental Science and Technology of the Autonomous University of Barcelona (ICTA-UAB). Providing a clearer picture of processes like gentrification, the research aims to inform a new direction for urban sustainability, in which social and racial equity are placed at the center of planning to produce green, healthy, and equitable communities. Anguelovski, Isabelle; Argüelles, Lucía; Baró Porras, Francesc; Cole, Helen; Connolly, James J. T.; García-Lamarca, Melissa; Loveless, Stephanie; Pérez-del-Pulgar, Carmen; Shokry, Galia; Trebic, Tatjana; Wood, Erin
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.017 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".