Case-comparison study protocol for gauging effects of neighbourhood trends and sickness: examining the perceptions of transit-Induced gentrification in Prince George’s County
Bibliographic record
Abstract
INTRODUCTION: Impoverished neighbourhoods and communities of colour often bear the brunt of unintended transit-oriented development (TOD) impacts. These impacts have been known to come in the form of transit-induced gentrification (TIG), a socioeconomic by-product of TOD defined as a phenomenon that occurs when the provision of transit service, particularly light rail transit (LRT), 'up-scales' nearby neighbourhood(s) and displaces existing residents. Consequently, TIG or even the perception of TIG can impact health outcomes (eg, anxiety) and social determinants of health (SDOH) (eg, crime). METHODS/ANALYSIS: In 2022, the purple line (PL), a 16.2 mile LRT line, is opening in Prince George's County, Maryland, a suburb of Washington, DC, comprised of over 80% African American and Hispanic residents. By taking advantage of this natural experiment, we are proposing the GENTS (Gauging Effects of Neighborhood Trends and Sickness: Examining the Perceptions of Transit-Induced Gentrification in Prince George's County) Study in order to evaluate perceived TIG and associated health outcome and SDOH changes, at two points in time, among Prince George's County adults in a prospective case-comparison design during the pre-PL LRT period. Descriptive analysis and latent growth curve modelling will be used to examine these changes over time. ETHICS AND DISSEMINATION: Ethics approval has been granted by the University of Maryland Institutional Review Board. The GENTS Study will identify temporal changes in perceived TIG, health outcomes and SDOH among case and comparison residents before the completion and operation of the PL LRT, an under researched period of TOD. The dissemination of GENTS Study findings will be able to address research questions and policy issues that are specifically tailored to PG County while also providing more effective procedural solutions for other regions undergoing TOD and TIG risks.
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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 | 0.065 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.073 | 0.009 |
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".