Graying arts access: crafting creative online programming to promote older adults' artistic engagement in and beyond pandemic time
Bibliographic record
Abstract
Introduction: Declared a global pandemic by the World Health Organization (WHO) in March 2020, the COVID-19 virus and attendant patchwork of local, regional, and national government-initiated public health responses to it unexpectedly opened possibilities for greater access to culture for disabled and chronically ill people in ways that were unimagined in pre-pandemic times. During the "emergency" period of the pandemic, the fields of critical disability studies and aging studies independently demonstrated the importance and value of shifting to digital technologies for disabled people and older adults respectively; however, to date, little scholarship has considered the value of digital technologies for older adults aging with and into disabilities beyond pandemic time. Methods: Informed by the theoretical insights of scholarship exploring critical access and the aging-disability nexus, this paper draws from empirical data collected during Phase 2 of Direct[Message]: Digital Access to Artistic Engagement, a collaborative, community-based, arts-informed research project based in Southwestern Ontario (Canada). Drawing from 50 qualitative interviews with aging adults from un/under/represented communities, findings explore the intersections of older age and disability, including dynamics related to gender, sexuality, migration, size, race/ethnicity, and other differences, as these relate to access to and enjoyment of creative spaces before, during, and "after" the COVID-19 pandemic. Results: Results show that older adults aging with/into disabilities in Southwestern Ontario express an overwhelming desire and even urgent need to access interactive arts programming from the relatively safe spaces of their homes both within and outside pandemic time. Discussion: As the normative world pushed for a return to ableist normative life in 2022, a year marked by "severe" rates of the highly infectious Omicron variant and the loss of effective public measures, such as community masking and widely available testing, participants described the need for continued access to creative and social participation via remote options that sidestepped socially exclusive and physically inaccessible spaces. Findings indicate a need for increased investment in digital arts programming for older adults aging with/into disabilities.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".