Bibliographic record
Abstract
The inclusion of older people in research is a relatively new concept, predominantly explored in the UK, US, and Canada. There are a number of approaches to this type of research, and this plurality is apparent in the terminology appearing in the academic literature. “Inclusive research” overlaps with terms such as “user-led research”, “community research”, “participatory action research” (PAR), “collaborative research” and “co-research”. In this book, we decided to use the umbrella term “participatory approaches” to refer to different approaches that aim at including older adults as co-creators of the research cycle. A growing body of evidence shows that what we deemed “participatory approaches” may increase the relevance and applicability of the research. Contributions provided by older people when designing, implementing, and disseminating research may make studies more effective, credible, and cost effective. Additionally, there is growing acknowledgement of a moral imperative to involve individuals who are the focus of research about them. However, participatory approaches among many benefits imply also numerous challenges; critical concerns include the potential for marginalisation of older people from deprived communities who risk being labelled as uncooperative or unproductive if they do not volunteer to get involved in community projects. This might partially explain why the involvement of older people as co-producers of the research process is not yet common practice. In order to address this challenge and make participatory approaches in ageing research more accessible to academics, practitioners, policy makers, and all who are interested in them, this chapter presents an overview of the field of participatory approaches in ageing research. We start by discussing common understandings and definitions of participatory approaches in ageing research and then proceed to their application across different research fields and research stages. Finally, we outline the structure of the book and the individual chapters.
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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 | 0.117 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.013 | 0.061 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".