Greening plans as (re)presentation of the city: Toward an inclusive and gender-sensitive approach to urban greenspaces
Bibliographic record
Abstract
Cities around the world are increasingly expanding their sustainability agendas and adopting urban green and blue infrastructure planning as a strategy to become more resilient, healthy and sustainable.However, the development of urban greening governance often lacks a holistic vision that considers social inequities within the planning, implementation and management of green and blue spaces.Further, gender inequities have been a specific dimension particularly overlooked in urban greening planning, despite gender concerns gaining increasing political relevance in recent years.In this research, we assessed the extento to which social and gender equity are being considered in urban greening plans and projects at the local level.We chose Barcelona (Spain) as main case study due to its pioneering role in implementing crosscutting equity and gender policies at the municipal level.Building on document analysis and semi-structured interviews with key stakeholders, we examined how social justice and gender are understood and operationalized in practice, from the design phase to implementation and maintenance of greening projects.Our findings suggest a shift in the role of urban greening which evolved from an ornamental role to a multifunctional vision of greening and is recently incorporating equity and inclusivity concerns.We identified three action areas of inclusive, gender-sensitive urban green planning practices: first, the incorporation of inclusivity and care as guiding visible values to recognize multiple needs of city residents; second, urban design for different uses and perceptions of greenspaces, particularly in relation to accessibility and autonomy; and third, the awareness and expertise from municipal staff vis-à-vis the consideration of social and gender equity in green planning and participatory approaches.Finally, we provide practical examples of the strategies that the City of Barcelona is implementing in each area and discuss some challenges and limitations, including what we identify as ad hoc intersectional greening.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".