Exploring the equitable inclusion of diverse voices in urban green design, planning and policy development: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Despite the increasingly evidenced positive impacts of green space on human physical and mental health, green spaces remain inequitably distributed across different socioeconomic groups. Urban planning and design should prioritise the development and maintenance of urban green spaces, especially for vulnerable and marginalised populations while thinking about protecting them from the effects of green gentrification. This scoping review will explore how the concepts of equity, diversity and inclusion are integrated into the design, the planning and policy development of urban green spaces. Also, we will explore what are the efforts made to incorporate equity, diversity, and inclusion concepts into the planning, design and policy development of urban green spaces to make them equitable for vulnerable and marginalised populations. METHODS AND ANALYSIS: . The search will be done in conjunction with a professional librarian, to include studies in all languages. The review will include multidisciplinary databases: Ovid MEDLINE, Ovid EMBASE, CINAHL, Web of Science and GeoBase. The search will be done from each database's inception to February 2024. We will present our results narratively and will conduct a thematic analysis using the urban green equity framework. This framework will guide our understanding of the interplay between the spatial distribution of urban green spaces and the recognition of diverse voices in urban greening decision-making. ETHICS AND DISSEMINATION: This scoping review will not require ethical approval since it will be collected from publicly available documents. The results of this scoping review will be presented as a scientific article, social media and public health or environmental conferences organised by environmental organisations or academic institutions.
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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 | 0.190 | 0.178 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.058 | 0.019 |
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".