ENHANCING KNOWLEDGE MOBILIZATION AND COMMERCIAL OUTCOMES IN AGING AND TECHNOLOGY
Bibliographic record
Abstract
A globally aging population necessitates innovative approaches for the development of technologies to ensure older adults age well. Whilst scientists across disciplines address a wide-range of ‘aging complexities’ through research and innovation, without appropriate integration of commercialization mechanisms, such outputs may result in little or no impact. To implement commercialization effectively requires integration and synthesis of experiences and working practices of diverse intersectoral professional, academic, and community stakeholders. This presentation demonstrates how Innovation Workshops designed using the principles of transdisciplinarity facilitated the development of commercialization strategies to improve knowledge mobilization and commercial outcomes in aging and technology. We discuss key strategies of knowledge translation, such as effective commercialization, dissemination, and evaluation regarding communication and utilization of workshop materials and information. Results of the 3-month post-workshop survey, focus groups, reflexive summaries, and field notes highlight the importance and challenges of evaluating impact through implementation and evaluation of this knowledge mobilization initiative.
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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 | 0.040 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".