RESEARCH TO STRENGTHEN, INNOVATE, AND TRANSFORM AGE-FRIENDLY COMMUNITY PRACTICE
Bibliographic record
Abstract
Abstract There is much research being conducted to better understand and advance age-friendly community practice. This symposium presents research from leading age-friendly researchers and practitioners across the United States. Drs. Black and Oh provide an analysis of the nation’s sectoral efforts based on progress reported by the age-friendly communities. Drs. Hernandez and Coyle will describe the research and community engagement that led to the development of an aging equity conceptual framework and examples of how it is being operationalized in the City of Boston. Drs. Greenfield and doctoral student Pope will present on a scoping review of studies in the U.S. and Canada on the range of ways in which the public sector participates in age-friendly community efforts. Drs. Coyle and Oh and doctoral students Gleason and Somerville present on a study that explored factors inhibiting communities from officially joining the age-friendly network. Dr. Perry reports on efforts to elevate the voice of older adults on social justice issues pertaining to aging in place in the domain of housing. Individual abstracts provide further detail on each study’s methods and findings.
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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 | 0.052 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".