To what extent do urban forest and green space plans include housing-related content?
Bibliographic record
Abstract
Housing impacts (or is impacted by) the abundance and accessibility of urban forests and green spaces (UFGS) in multiple ways. However, these impacts are often overlooked in practice due to organizational siloing. This study analyzed 112 UFGS planning documents from Canada and the US to determine (1) whether they considered any housing impacts; (2) what housing-related challenges and opportunities they identified; and (3) whether these challenges/opportunities have evolved. Most plans considered at least one housing impact, and most impacts were related to (limited) money and space. The most common impacts were: (1) UFGS increase property values; (2) some residences lack UFGS access; (3) residential development provides funding/space for UFGS; and (4) housing demand increases pressure on existing UFGS. Less common were homelessness in UFGS; using housing-related taxes to fund UFGS; and green gentrification. Interestingly, not all plans agreed on whether an impact was an opportunity or a challenge. For example, UFGS increasing property values was often seen as a benefit for homeowners and local governments. However, recently it has been seen as a disbenefit, if it leads to gentrification. Future research and/or practice needs to carefully consider whether housing-related impacts are an opportunity or a challenge in their given context. Further suggestions include (1) implementing/evaluating housing affordability measures alongside greening; (2) establishing funding sources for greening independent of residential development; (3) developing/evaluating innovative strategies to green densifying areas; (4) developing regulations, incentives, and supports for greening private property; and (5) establishing meaningful collaborations with housing, land-use, and/or social services departments/organizations.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".