Resident Attitudes and Actions Toward Native Tree Species: A Case Study of Residents in Four Southern Ontario Municipalities
Bibliographic record
Abstract
Abstract Urban forests are increasingly acknowledged as important areas for producing ecosystem services and maintaining ecosystem processes. In response, municipalities throughout North America have been adopting long-term plans to support strategic management of the urban forest. These plans have the potential to shape the urban forest for decades to come. Most management plans emphasize the planting of native trees, to improve ecological integrity and ecosystem services, and acknowledge the need for resident stewardship to help meet urban forestry goals. Residents’ support and action is crucial, since the majority of urban trees are located on residential property, yet it is unclear what residents’ attitudes and actions are regarding native trees. Using a case study of four municipalities in southern Ontario, Canada (two that have management plans that call for more native species plantings and two that do not), researchers administered a survey that explored residents’ attitudes and actions toward native tree species, focusing on the relationship between municipal emphasis on native species planting, household socio-demographics, and residents’ attitudes and actions toward native species. The results indicate that residents’ generally have positive attitudes toward native trees, although fewer are interested in planting native species if they create a hazard or increase costs. Moreover, these generally positive attitudes do not translate into emphasizing native species when actually selecting tree species to plant. This paper adds to existing research surrounding the need for further outreach and environmental education and greater availability of native plants in local nurseries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".