The best laid plans: How do adopted city sustainability goals influence site-level action in urban forestry?
Bibliographic record
Abstract
Cities increasingly hope to mobilize nature-based solutions in urban sustainability planning because of their wide-ranging ecosystem services. In this critical moment, where failure due to poor implementation could lead policymakers to turn away from nature-based solutions, we investigate the case of urban forestry and ask: how do strategic-level sustainability goals influences site-level decision-making? We conducted semi-structured interviews with 20 urban foresters leading advanced programs at municipalities with adopted sustainability goals, framed as ecosystem services, across the US and Canada. Day-to-day, site-level decision-making focused on advancing a "more, bigger trees" paradigm with the justification that this would deliver increased ecosystem services in a general sense and thus contribute to city goals. Our analysis and results suggest that urban foresters are more guided by a shared sense of the greater purpose of their work to serve urban communities than city plans, which yields actions usually aligning but sometimes conflicting with specific strategic-level goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".