PLANNING ABOUT US, AND BY US: REFLECTIONS ON WATERLOO’S COLLABORATIVE AGE-FRIENDLY INITIATIVE
Bibliographic record
Abstract
Among policy makers and academics, the necessity to translate age-friendly community planning principles into sustained action is, by now, well established. University researchers are accustomed to thinking in terms of interdisciplinary collaboration and are now increasingly encouraged to engage in ‘scholarship of practice’ by conducting research and policy making collaboratively with public sector and community partners. While reflections on the factors that support interdisciplinary academic research are common, assessments of the management multi-sector collaborative work are less so. This presentation will present a critical reflection on the ‘multi-stakeholder’ work of the City of Waterloo’s Age-Friendly Multi-Agency Advisory Committee involving the collaboration of university, public sector and community partners, and led by community older adults. The presentation will address the factors that have contributed to, or challenged implementation success and highlights the need for governments to invest in the social capital needed to sustain age-friendly initiatives led by older adults.
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.045 | 0.028 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.013 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 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".