Comparison of Canadian urban forest perceptions indicates variations in beliefs and trust across geographic settings
Bibliographic record
Abstract
Urban forests are characterized by relationships between people and trees, where urban trees provide benefits to people and people make decisions impacting trees. People’s perceptions of urban forests are related to the cognitive processes that underpin benefits received from trees, while also influencing support for or against trees and their management. A growing literature has considered urban forest perceptions, but most studies are limited to a single geographic area and focus on socio-economic influences, with less consideration of location and cultural influences. This study explores the relationship between where people live, the language they speak, and multiple perception responses associated with urban forests (i.e. values, beliefs, trust, satisfaction) to better understand commonalities and differences across distinct geographic settings and populations. We conducted an online survey about urban forest perceptions in three Canadian urban regions, allowing us to explore perceptions between regions, locations on an urban gradient and language spoken. We found geographic and language differences primary for beliefs held about urban trees and trust in municipal government’s decision-making about those trees, while values and satisfaction with trees and their management were more stable across geographic settings and language spoken. Our findings suggest that some perceptions vary between populations. Additionally, our findings reinforce the need for urban forest managers to understand the specific perceptions held by different populations, rather than assume universality of perception, to ensure specific and differential urban forest management objectives are in place to supports local people and ecological elements.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".