Measuring and modelling values, beliefs and attitudes about urban forests in Canada and Australia
Bibliographic record
Abstract
Nature-based solutions are informed by how communities think about nature. However, research on how urban communities think about urban nature is seldom carried out across urban contexts. In doing so it can be useful to select specific aspects of urban nature, such as urban forests and urban trees. Our study responds to these needs by measuring the cognitive constructs of values, beliefs, and attitudes towards urban forests and modelling their relationships using a representative survey of >3400 residents living across two different urban contexts: Toronto, Canada, and Melbourne, Australia. Means difference, generalized linear regression, and structural equation analyses, were used to test how values, beliefs, and attitudes differed between metropolitan areas, and how they related to other cognitive constructs, social-ecological context, and demographic factors. We found that resident values and beliefs (more abstract and general constructs) about urban trees were similar across metropolitan areas, but some attitudes (more specific and variable constructs) were different between metropolitan areas, including residents' level of trust in how municipalities manage urban forests and their level of satisfaction with trees and their management. Female residents, and residents who had higher levels of nature relatedness and subjective wellbeing, valued urban forests more. Values, beliefs, and knowledge of trees were significant drivers of resident satisfaction with trees and their management. We discuss implications for urban nature policies. • We evaluated people's values, beliefs, and attitudes associated with urban forests. • We compared representative data between cities, Melbourne and Toronto. • Values and beliefs were similar across cities. • Attitudes, including trust in cities and satisfaction with trees, varied across cities. • Understanding how communities think about urban nature can lead to better policies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".