Bibliographic record
Abstract
The primary objective of this book was to provide an in-depth, multi-disciplinary knowledge base for participatory approaches in ageing research. In this final chapter, we summarise the main lessons learned from the contributions in this book and encourage readers to think about the potential future(s) of participatory approaches in ageing research. Drawing on insights from over 25 projects from Australia, Canada, Europe, India, New Zealand, South Africa and the USA, these learnings arise from different disciplinary perspectives, a wide variety of applied methods and socio-geographical contexts of participatory approaches in ageing research. The findings drawn from them are highly relevant both for research and practice, ranging from the design of ageing policies to product and service development, urban and landscape planning, health, care and social work. From our perspective, the following aspects play a crucial role in this: heterogeneity of older adults; global transformations in modern societies; participation of older adults in policy-making and products/services’ designs; ageism; as well as the need for flexibility and adaptability in order to successfully co-create research. Instead of giving answers, we conclude with micro, meso and macro-level questions that ageing research may tackle in the future.
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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 | 0.024 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".