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 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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".