Older Adults and Gentrification: The Positive Role of Social Policy
Bibliographic record
Abstract
Supportive public policies are suggested as ways to lessen gentrification's impact for older adults. While explicit policies designed to help older adults with gentrification are rare, literature on age-friendly cities is a close proxy. We utilized three North American cases undergoing gentrification: New York City, NY, and Denver, CO, in the United States and Hamilton, in Ontario, Canada, to present existing neighbourhood-based policies as social determinants of health in housing, resource access, healthcare, transportation, and communal places. Age-friendly policy application gap examples and COVID-19's impact were included. Using a qualitative comparative case study method, we found policies were not specifically designed to address older adults' gentrification needs. With the call for age-friendly designations, the role of gentrification in neighbourhoods with older populations must be included. We call for gentrification-specific policies for older adults to provide greater safeguards especially when events such as COVID-19 compete for existing, over-stretched resources.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".