Exploring University Age-Friendliness Using Collaborative Citizen Science
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Since the launch of Dublin City University's Age-Friendly University (AFU) Initiative in 2012, relatively little empirical research has been published on its feasibility or implementation by institutions of higher learning. This article describes how collaborative citizen science-a research method where professional researchers and community members work together across multiple stages of the research process (e.g., data collection, analysis, and/or knowledge mobilization) to investigate an issue-was used to identify barriers and supports to university age-friendliness at the University of Manitoba (UofM) in Canada. RESEARCH DESIGN AND METHODS: Ten citizen scientists each completed 1 data collection walk around the UofM campus and used a tablet application to document AFU barriers and supports via photographs and accompanying audio commentaries. The citizen scientists and university researchers then worked together in 2 analysis sessions to identify AFU priority areas and brainstorm recommendations for institutional change. These were then presented to a group of interested university stakeholders. RESULTS: The citizen scientists collected 157 photos documenting AFU barriers and supports on campus. Accessibility, signage, and transportation were identified as being the most pressing issues for the university to address to improve overall age-friendliness. DISCUSSION AND IMPLICATIONS: We suggest that academic institutions looking to complete assessments of their age-friendliness, particularly those exploring physical barriers and supports, could benefit from incorporating older citizen scientists into the process of collecting, analyzing, and mobilizing 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 | 0.020 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 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".