Perceived Transit-Induced Gentrification, Walkability and Crime: An Examination of the Purple Line Light Rail Transit in Prince George’s County, Maryland
Bibliographic record
Abstract
This study investigates perceived transit-induced gentrification (TIG) in anticipation of the Maryland Purple Line light rail transit among residents of Prince George’s County, Maryland, and its associations with walkability and crime. In spring 2021, Wave I of the GENTS Study collected data through an online questionnaire on residents’ perceptions of the Purple Line and related neighborhood factors. Using exploratory factor analysis and multiple linear regression, we examined the relationships between perceived TIG, walkability, and crime. In a sample of 465 respondents, primarily Black/African American (61%) and White (28%), walkability factors such as accessibility and a pedestrian-friendly environment were significantly associated with positive TIG perceptions. In contrast, concerns about house break-ins, purchasing a gun, and walking barriers were linked to negative TIG perceptions. These findings contribute to a deeper understanding of the complex dynamics between urban development, gentrification, and social determinants of health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".