Bibliographic record
Abstract
This chapter brings attention to the mechanisms of ageism in the design and implementation of socio-technical interventions targeted at older adults. Drawing on findings from Digital Storytelling workshops with six 80+-year-olds at care homes in Japan and Canada, the chapter identifies contexts and conditions that undermine the agentic involvement of older participants in intervention studies. While the Digital Storytelling workshops generated positive outcomes and created opportunities for participants to redefine themselves and influence others, there were power imbalances in the story production phase. Engaging with an actor-network approach, the interplays of technologies, built environments and institutionalised concepts of old age are examined. Findings show that the empowering potentials of Digital Storytelling were contingent on participants negotiating and confronting forms of age discrimination that reverberate through technologies, care services, facilitators&s; expectations, and participants&s; own self-perceptions. Where many socio-technical interventions have failed to meaningfully involve older adults and have upheld stereotyped views of older adults&s; technological needs, this chapter forges connections between key factors that influence older adults&s; positioning within socio-technical interventions.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".