COMMUNITY ENGAGEMENT IN DIFFERENT NATIONAL CONTEXTS: BUILDING BRIDGES AND AGE-FRIENDLY ENVIRONMENTS
Bibliographic record
Abstract
Abstract To bridge the gap between research and practice, initiatives to facilitate knowledge mobilization in tandem with community engagement have become increasingly important. This symposium will advance the understanding about knowledge mobilization processes and community engagement efforts by offering insight from scholars from established research centers with long-standing community engagement as well as new initiatives connecting disparate disciplines and sectors. Representing different national contexts, the speakers will share their experiences regarding the complexities of partnership and engagement approaches, processes, and activities. Benefits (research quality, relevance, utilization, transgressing boundaries, addressing complex problems) as well as barriers (diverse conditions/cultures, lack of time/resources, multiple levels of terminology, hard-to-reach groups, projectification, boundaries of traditional science systems) will be addressed. The first speaker will describe the development and establishment of a pool of interested parties in Sweden to systematize and facilitate communication, entryways to research studies, and user involvement in aging research. The second speaker will describe the work of a center serving older African Americans from Detroit, Michigan, including innovative leadership opportunities created for its members. The third speaker will describe new initiatives in the Canadian context that purposefully choreograph knowledge mobilization activities to disrupt disciplinary and industry silos in the practice and research of salutogenic/healthy long-term care environments. The last speaker will describe an engagement effort towards age-friendly infrastructures in rural municipalities in Canada. Finally, discussant Ian Johnson from the University of Texas San Antonio will identify, compare, and discuss underlying themes, commonalities, differences, and lessons learned generated from the presentations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".