Greenness and the Potential Resilience to Sexual Violence: “Your Neighborhood Is Being Neglected Because People Don’t Care. People With Power Don’t Care”
Bibliographic record
Abstract
There is increasing evidence that green space in communities reduces the risk of aggression and violence, and increases wellbeing. Positive associations between green space and resilience have been found among children, older adults and university students in the United States, China and Bulgaria. Little is known about these associations among predominately Black communities with structural disadvantage. This study explored the potential community resilience in predominately Black neighborhoods with elevated violent crime and different amounts of green space. This embedded mixed-methods study started with quantitative analysis of women who self-identified as "Black and/or African American." We found inequality in environments, including the amount of green space, traffic density, vacant property, and violent crime. This led to 10 indepth interviews representing communities with elevated crime and different amounts of green space. Emergent coding of the first 3 interviews, a subset of the 98 in the quantitative analysis, led to a priori coding of barriers and facilitators to potential green space supported community resilience applied to the final 7 interview data. Barriers were a combination of the physical and social environment, including traffic patterns, vacant property, and crime. Facilitators included subjective qualities of green space. Green spaces drew people in through community building and promoting feelings of calmness. The transformation of vacant lots into green spaces by community members affords space for people to come together and build community. Green spaces, a modifiable factor, may serve to increase community resilience and decrease the risk of violence.
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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 | 0.004 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".