Defiance, Resilience, And (Mis)Representation
Bibliographic record
Abstract
There is an abundance of research that reveals our society’s stereotyping of older adults and the negative cultural images of the process of ageing (DeMuth, 2004; Fealy et al., 2012). These stereotypes–disinformations–that are attached to older individuals suggest that as we age, we enter a stage of stagnation, decline, and decreased resilience. However, there is substantial evidence that as people age, they still maintain considerable adaptability, mental resilience, and overall well being (Friedan, 1993, as cited in DeMuth, 2004). Rise, Shine, Sing! is a weekly online research-creation program that investigates how music, dance, and theatre can affect and contribute to the resilience and wellbeing of older adults. Through our work as research assistants on this project, we have observed the leaps and limitations of music theatre participants within our research community. Drawing from our experiences with the program, and reflecting upon our positionality in our research contexts, we will explore the ways in which misinformation and disinformation affect older individuals and the limitations placed on them by both society and themselves. Specifically, we will confront the stereotypes associated with older adulthood, the ageism present in the theatre industry, and resilience through the ageing process. At the same time, we will examine the ways that intergenerational communities, the importance of pursuing new passions, and creating opportunities to be expressive can enhance the lives and PERMA wellbeing of older adults. Our collective research and work with Rise, Shine, Sing! suggests that older adulthood can be a time of vibrancy, creativity, and increased agency.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.024 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 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".