Why Can’t We All Just Get Along? Conflict and Collaboration in Urban Forest Management
Bibliographic record
Abstract
Abstract Urban forest management is crucial for supporting human well-being, ecosystems, and society, particularly with expanding global urban population and multi-uses of these urban greenspaces. This literature review examines the conceptualization and factors that contribute to conflicts and/or collaborations in urban forest management, including, but not limited to, diverse actors’ uses, needs, and perceptions. Using PRISMA methods, we systematically reviewed 176 scholarly articles published between 2013 and 2021 and found that most articles were primarily from the United States, Australia, and Canada. Findings highlight the need for clearer definitions of collaboration, emphasizing communication, operational tasks, planning, and shared beliefs among actors. Positive collaborations involved multi-level engagement and inclusive decision-making. In most cases, multiple issues contributed to conflict, including a variety of stakeholders with differing viewpoints on a given situation. Conflicts are commonly complex situations that do not lend themselves to a one-size-fits-all solution and tend to be a unique manifestation of the people, places, and perspectives involved. Our review can inform practitioners about more inclusive practices and adaptive management of urban forests. We conclude by providing lessons learned and suggestions for future research on stakeholder involvement, public education, governance, policy, decision-making, and the role of biophysical and ecosystem services in urban forest collaboration and conflicts.
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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 | 0.001 |
| 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".