MOBILIZING UNTAPPED LIVED EXPERTISE TO ASSESS ACCESSIBLE AND AGE-FRIENDLY INFRASTRUCTURES
Bibliographic record
Abstract
Abstract Many rural municipalities in Canada have been experiencing steady population declines overall, while vulnerable population groups such as older adults and persons with disabilities tend to remain in their communities. With shrinking tax bases, municipalities must make smarter choices in provisions of accessible and age-friendly services and amenities. However, exact conditions and needs of accessible and age-friendly infrastructures in rural communities are increasingly becoming difficult to evaluate, as their planning departments do not have sufficient human and financial capacities to monitor them. The Planning for Equity, Accessibility, and Community Health (PEACH) Research Unit is situated in a planning school at a university in Nova Scotia, Canada. In partnership with two rural municipalities, PEACH Research Unit conducted in-person and online consultations with persons with lived expertise to develop key indicators that can inform municipalities on priority accessible and age-friendly infrastructure needs. This presentation will highlight lessons learned from our engagement processes, including considerations for accessibility and inclusive recruitment, engagement venues, and communication methods that maximize participation of a hard-to-reach group in rural communities. An example tool especially useful for our purpose was an online mapping platform, which helped the participants articulate their insights and better share stories of their experiences. It facilitated discussion beyond simply inventorying what types of infrastructure matter to them—while also soliciting their suggestions on solutions to the design of specific sites in real settings, which were often relational to surrounding environments.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".