Who participates in green infrastructure initiatives and why? Comparing participants and non-participants in Philadelphia’s GI programs
Bibliographic record
Abstract
Green infrastructure (GI) refers to trees, rain gardens, rain barrels, and other features that address stormwater management, climate change and other challenges facing many cities. GI is often not equitably distributed across urban landscapes, making its benefits unevenly experienced. Cities have multiple initiatives focused on different types of GI in residential areas, including underserved neighborhoods, although there is potential for GI programs to serve more privileged neighborhoods. The goal of this study was to examine GI program participants and non-participants to better understand who participates in different types of residential GI programs and why. We surveyed residents who had previously participated in Philadelphia’s GI programs as well as those who had not, comparing socio-demographics, knowledge-levels, environmental concerns, outdoor space preferences, motivations and barriers. We found that the GI program participants are on average younger, wealthier, more highly educated, and more likely to be White than our sample of residents who have not participated. Participants in tree programs have different socio-demographics and motivations as compared to those who installed green stormwater infrastructure. Future research should examine strategies to reach neighborhoods with different socioeconomic conditions and built environment characteristics, such as offering features appropriate for small properties with limited plantable space.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".