Cultural Background and Landscape History as Factors Affecting Perceptions of the Urban Forest
Bibliographic record
Abstract
Abstract Because a large proportion of the urban forest grows on private property, it is necessary to have broad community support for urban forestry. As people from all over the world live in Canadian cities, it was hypothesized that people with different cultural backgrounds would have different perceptions of the urban forest. This hypothesis was tested by (1) researching different landscaping traditions; (2) interviewing members of four different communities; and (3) conducting vegetation inventories. Inventory and interview data provided a consistent picture of the four communities. The British community reacted the most positively to shade trees. They also expressed the greatest willingness to plant shade trees, had the most shade trees per square meter on their properties, and were the only group that liked naturalized parks (hiking paths). The Chinese community showed less yard maintenance than the other communities, and many of the Chinese indicated that they did not want to add trees to their property. The Chinese responded more favorably than the other groups to photographs depicting landscapes free of trees. Italian and Portuguese communities emphasized fruit trees and vegetable gardens, and responded negatively toward shade trees when these were in conflict with their gardens. These cultural differences are largely consistent with the traditional use of trees in British, Mediterranean and Chinese landscaping, and appear to be maintained among North American immigrant populations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".