Intention to Install Green Infrastructure Features in Private Residential Outdoor Space
Bibliographic record
Abstract
Green infrastructure (GI) features in private residential outdoor space play a key role in expanding GI networks in cities and provide multiple co-benefits to people. However, little is known about residents' intended behavior concerning GI in private spaces. Resident homeowners in Toronto (Ontario, Canada) voluntarily participated in an anonymous postal survey (n= 533) containing questions related to likelihood to install additional GI features in their private outdoor space; experiences with this space, such as types of uses; and environmental concerns and knowledge. We describe the association between these factors and people's intention to install GI in private residential outdoor space. Factors such as environmental concerns and knowledge did not influence likelihood to install GI. However, experiences with private residential outdoor space, such as nature uses of this space, level of self-maintenance of this space, and previously installed GI features, were significant influences on the likelihood to install GI. These findings have important implications for managing GI initiatives and the adoption of GI in private residential spaces, such as orienting communication materials around uses of and experiences with outdoor space, having programs that generate direct experiences with GI features, and considering environmental equity in such programs.
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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 | 0.000 | 0.000 |
| 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) | 0.005 | 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".