Small decisions as social and ecological leverage points for cities to meet sustainability goals: A synthesis for urban forestry research and practice
Bibliographic record
Abstract
While urban forest management is increasingly held up as a nature-based solution to myriad urban challenges, major gaps exist in research and practice in the links between decision-making at different scales and their impacts on different dimensions of sustainability. Where are the key levers to change the future of urban forests, and set cities on more sustainable pathways? Using street trees as a generalizable, focal unit of analysis, the Street Tree Futures research project explored urban foresters’ influence on strategic goals, particularly goals to increase ecosystem services. In the context of the United States and Canada, we explored how urban foresters are able to effect change through direct tree-level management (i.e. decisions which physically affect trees), as a necessary precursor to improving indirect management (e.g. policies, education, outreach). This article is a synthesis of a series of studies examining these issues from multiple dimensions and culminating in a focus group of cross-sector urban forestry experts. Overall, we establish that often undervalued small, site-level, decisions are not only necessary for urban tree survival and growth but are also key points of influence where urban foresters’ expert knowledge contribute to large-scale strategic sustainability goals. Overlooking site-level decisions leads to missed opportunities to foster interdisciplinary collaboration, and most importantly, advance towards multiple critical sustainability goals. • Urban foresters are often isolated from other sustainability efforts in their cities. • Practitioners sum up this common mentality as “my goals vs. their goals”. • Urban forest management decisions can align with strategic sustainability goals. • Urban tree life cycle phases are leverage points for different sustainability goals. • These leverage points are opportunities for interdisciplinary collaboration.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".