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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".