Governing for Diversity: An Exploration of Practitioners' Urban Forest Preferences and Implications for Equitable Governance
Bibliographic record
Abstract
Urban forests are increasingly acknowledged as sources of multiple benefits and central to climate resilience and human wellbeing. Given these diverse and significant benefits, it is important to govern urban forests so as to ensure that all residents have equitable access and enjoyment. Understanding urban forest preferences, and including them in planning and management, is a key aspect of informed and contextually-relevant urban forest governance. Although many studies have examined public urban forest preferences, we lack an understanding of the preferences of a key stakeholder: urban foresters. This study presents the results of semi-structured interviews with 22 urban forestry and allied green practitioners focused on preferred and least-preferred aspects of the urban forest. Participants expressed their preferred urban forest characteristics according to four themes: administration, spatial attributes, naturalness, and social benefits. Least-preferred characteristics were expressed under the themes of administration and degradation. Results suggest that practitioners employ a systems-level lens when discussing urban forest preferences, in contrast to the general public. However, they also draw on personal experience when constructing their preferences, particularly in relation to naturalness and spatial diversity. These results highlight the importance of recognitional green equity and mosaic governance in urban forestry, to facilitate a just balance amongst the diverse preferences of urban forest stakeholders.
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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 | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".