Exploring Relationships between Socioeconomic Background and Urban Greenery in Portland, OR
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
Do urban residents experience societal benefits derived from urban forests equitably? We conducted a broad-scale spatial analysis of the relationship between urban greenery and socioeconomic factors in the Portland metropolitan area. The Normalized Difference Vegetation Index was derived from National Agriculture Imagery Program images to map urban vegetation cover, and Outdoor Recreation and Conservation Area data were used to identify green spaces. These measures of urban greenery were correlated with census data to identify socioeconomic factors associated with high levels of green inequity. Population density, house age, income, and race were strongly correlated with vegetation cover. However, the distribution of green spaces showed a much weaker relationship with socioeconomic factors. These results highlight the importance of different measures of access to urban greenery and suggest potential solutions to the problem of urban green inequity. Cities can use our methods to conduct targeted urban forest management to maximize urban forest benefits received by residents.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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