Age-Friendly Rural Communities: Conceptualizing ‘Best-Fit’
Bibliographic record
Abstract
The literature on age-friendly communities is predominantly focused on a model of urban aging, thereby failing to reflect the diversity of rural communities. In this article, we address that gap by focusing on the concept of community in a rural context and asking what makes a good fi t between older people and their environment. We do this through (a) autobiographical and biographical accounts of two very different geographical living environments: bucolic and bypassed communities; and through (b) analysis of the different needs and resources of two groups of people: marginalized and community-active older adults, who live in those two different rural communities. We argue that the original 2007 Health Organization definition of age friendly should be reconceptualized to explicitly accommodate different community needs and resources, to be more inclusive as well as more interactive and dynamic, incorporating changes that have occurred over time in people and place.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 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".