Socioeconomic and race/ethnic disparities in observed park quality
Bibliographic record
Abstract
BACKGROUND: Though park presence and access disparities are well studied for their associations with physical activity (PA), disparities in the availability and quality of amenities and facilities within parks have been infrequently examined. METHODS: Five hundred forty-three parks from 472 block groups in the Seattle, WA and Baltimore, MD regions were audited using the Environmental Assessment of Public Recreation Spaces (EAPRS) to assess presence and quality (e.g., condition, cleanliness) of amenities (e.g., restrooms, seating) and facilities (e.g., fields, courts). General linear model regressions investigated Census 2000-derived neighborhood race/ethnicity and income main effect and interactive relationships with 7 park quality summary scores: 1) trails, 2) open space, 3) sports facilities, 4) PA facilities count, 5) PA facilities quality, 6) aesthetics, and 7) overall amenities, controlling for park size. The regions were analyzed separately due to differing race/ethnicity distributions. RESULTS: In the Seattle region, neighborhood income was significantly negatively associated with sports quality score (p < .043), PA facilities total count (p < .015) and the overall amenities quality score (p < .004) (unexpected direction). In the Baltimore region, neighborhood race/ethnicity (percent White/non-Hispanic) was significantly positively related to the open spaces quality score (p < .011) (expected direction). A significant income-by-race/ethnicity interaction was found for PA facilities quality (p = .014), with high-percent minority neighborhoods having higher quality parks in high- vs. low-income neighborhoods, yet was opposite in mostly White/non-Hispanic neighborhoods. The other income-by-race/ethnicity interaction was for overall amenities quality score (p = .043), where scores in high-percent minority neighborhoods were best in high- vs. low-income neighborhoods. There was little difference in scores within mostly White or mixed neighborhoods by income. CONCLUSIONS: Patterns of association of neighborhood race/ethnicity and income with park qualities differed between regions. In the Seattle region, "equitable differences" were found, where lower income neighborhoods had better park quality on average. In the Baltimore region, park quality was more consistently negatively associated with income and race/ethnic diversity, and complex interactions of race/ethnicity by income were detected. These findings emphasize the need to explore other factors that may explain variations in park quality, like local policy, citizen involvement in park decision-making, park funding and allocation, sources of funding and park priorities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".