Public Food Trees’ Usage and Perception, and Their Potential for Participatory Edible Cities: A Case Study in Birjand, Iran
Bibliographic record
Abstract
Public food trees are increasingly popular among researchers, urban planners, and citizens for their diversity of social, provisional, and environmental benefits. However, more research is needed to determine how to overcome their barriers. Here we used a qualitative approach to explore the usage and perception of public fruit trees and attitudes towards public usage and participation in two urban green spaces harboring fruit trees in Birjand, Iran: Tohid Park and Akbarieh Garden. Semi-structured interviews were conducted with twelve visitors to both spaces, four administrators, and eight workers. Almost all visitors had great personal experience and interest in picking fruit; nevertheless, usage in these green spaces was low, partly due to social norms. Almost all visitors appreciated the public fruit trees for the diverse pleasures that they provide (sensory, emotional, cognitive, experiential, social). Ten also had a high interest in participating in their management, especially in Tohid Park due to its greater social impact. Education and access limitation were suggested by some as important to reduce tree damage and maximize production. Our findings expand our understanding of how citizens relate to public urban fruit trees and can be involved in co-management schemes, thereby contributing to building smart and participatory edible cities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".