Taking Stock: The Current State of Urban Forestry Education at International Institutions of Higher Education
Bibliographic record
Abstract
Abstract Background Urban forestry has evolved over the past 50 years, growing into a distinct profession with expanding global initiatives and increasing funding. This paper examines the state of urban forestry education, analyzing current programs, competencies, and educational approaches. Methods We conducted a Delphi survey with urban forestry educators. The survey, distributed to educators in multiple countries, gathered data on teaching contexts and competencies. We then collected, described, and analyzed case studies from a diverse range of programs. Results Survey responses from 34 educators reveal diverse teaching backgrounds and subjects taught. The results show diverse competencies in areas such as urban forest management, environmental science, and community engagement. We also present 6 case studies showcasing innovative educational approaches, reflecting the field’s potential for future development. The case studies highlight varied educational models, from massive open online courses (MOOC) to specialized degrees, showcasing different approaches to curriculum and delivery. Key findings include a wide range of teaching topics and competencies, reflecting both the interdisciplinary nature of urban forestry and emerging educational trends. Conclusions This study examines the evolving landscape of urban forestry education. The discipline’s diversity is evident in the broad range of topics covered, from arboriculture to urban planning to human health. Urban forestry emerges as a nimble, transdisciplinary discipline with deep roots in forestry. We highlight the need for a cohesive, well-defined curriculum to advance the profession and educational standards.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".