Advancing green space equity via policy change: A scoping review and research agenda
Bibliographic record
Abstract
Urban green spaces – including parks, trees, and other vegetated areas – are inequitably distributed in cities worldwide, as underserved groups, such as low-income and people of color, have significantly lower provisions of such resources. Motivated by the health benefits of green spaces, advocates and policymakers in several countries have sought to ameliorate these systemic inequities by implementing green space equity initiatives. Many such initiatives are individual projects (e.g., a new park in an underserved neighborhood), but new policies have also been implemented to advance green space equity. To date, limited research has examined which policies have been implemented, what it takes to adopt and implement them, and whether they have effectively advanced green space equity. Based on a scoping literature review and a workshop with an interdisciplinary group of researchers, we developed a research agenda on green space equity policy. Our research agenda includes research questions grouped into four interrelated themes: policy impact and evaluation, power building and policy change, green gentrification, and health equity and climate change. The contributions of this paper are twofold: We synthesize current knowledge on green space equity policy and present a research agenda whose findings can inform policy work on green space equity. • Limited research has examined policies to advance green space equity. • We developed a research agenda on green space equity policy based on a scoping review and a workshop. • In the review, we identified research on green space equity policies, processes to adopt them, and their effectiveness. • Agenda themes include policy evaluation, power building, green gentrification, and health equity and climate change. • Research on green space equity policy speaks to global challenges such as health inequities and climate change.
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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 | 0.087 | 0.193 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.030 | 0.033 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".