Who participates in green infrastructure initiatives and why? Comparing participants and non-participants in Philadelphia’s GI programs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it