Intersections of Ageism and Homelessness Among Older Adults: Implications for Policy, Practice, and Research
Bibliographic record
Abstract
Ageism remains a key issue in gerontological literature and has long been recognized as a deeply harmful form of discrimination. Despite advances in ageism scholarship related to education, advocacy, and prevention, there are calls for ongoing intersectional examinations of ageism among minority groups and across older people facing multiple exclusions. In particular, very little ageism research has considered the experiences of age-based discrimination and prejudice among older people experiencing homelessness. We problematize this gap in knowledge and provide recommendations for policy, practice, and research to address ageist discrimination toward older people experiencing homelessness. Intersections of ageism and homelessness are summarized at four levels: intrapersonal, interpersonal, institutional/community, and societal/structural. Building upon the limited research, we recommend key strategies for supporting and protecting older people experiencing homelessness through the reduction of ageism at each level. We present these insights and recommendations as a call to action for those working in both the aging and housing/homelessness spheres.
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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 | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".