Determining Public Values of Urban Forests Using a Sidewalk Interception Survey in Fredericton, Halifax, and Winnipeg, Canada
Bibliographic record
Abstract
With the majority of Canada’s population concentrated in cities, it is important to determine what people consider important in urban nature. The concept of values can help illustrate what people consider important in urban nature beyond utilitarian considerations. This is the case for urban forests. However, many studies about public opinion on urban forests do not capture expressions of importance, focus on all the trees of the city, or provide respondents with a direct experience of urban forests. In Canada, most assumptions about Canadian urban forest values are based on results from the United States. In this study researchers present and analyze urban forest values data gathered with a sidewalk interception survey in the cities of Fredericton, New Brunswick; Halifax, Nova Scotia; and Winnipeg, Manitoba, Canada, to address some of these limitations. Respondents were asked to rate the level of importance of urban forests and mention the reasons. Results show that respondents rate the urban forest at a high level of importance and the reasons for this are aesthetics, air quality, shade, and naturalness, among other themes. There was a tendency for older people, women, and non-students to rate urban forests at a higher level of importance. Weather, related to time of year of survey delivery, has a discernible influence on the way value themes are distributed in the data. The study authors infer that this method helps capture data on respondents’ psychological states instead of their intellectual awareness as to what they consider important about urban forests.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".