Routledge International Handbook of Participatory Approaches in Ageing Research
Bibliographic record
Abstract
A living library is an event targeting interpersonal dialogue between the public and people with a certain (prejudiced) characteristic. In our version of the event, a team of colourful volunteers challenged people’s preconceptions on ageing, against the background of quantitative research which proposes seeing ageing not as a number, but as experiencing transitions. The goal was to create conversations, both between quantitative research and actual people’s narratives, and between the volunteers and the public, sharing experiences about life transitions. This chapter will highlight firstly what a living library event is, its methodology and practical organisation. Second, we analyse what happened during the event, structured around three talking points: People are intrigued by individual ageing stories, and try to draw comparisons to their own lives. Living libraries as a methodology work and are an attractive way to set up intergenerational and intercultural dialogue with a low threshold for participation. Third, we are conditioned to think about research impact in certain ways, emphasizing efficiency, targeting policy change, and being useful. Finally, we situate living library events at different points of the research life cycle. While its original intent was as a presentation of results to the public, a follow-up with the volunteers, as well as potential inclusion as informants and interview participants, followed naturally.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.064 | 0.034 |
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".