The literacies‐as‐events in the day of a life of an octogenarian: literacies of thriving as habits of a lifetime and (im)materially constituted
Bibliographic record
Abstract
Abstract Much is known about the literacies of early life, adolescence and some aspects of adulthood such as workplace literacies, but there has been a dearth of attention to the literacies of late life. The invisibility of these literacies has the potential to skew how curricula, pedagogy and policy developers understand and plan for literacies that can sustain people across the life course. It also can play into deficit discourses of elders, such as those prevalent during the COVID‐19 pandemic, that have led to a parallel pandemic of ageism. To reverse this invisibility, this study aimed to bring to light the everyday literacies of thriving elders and the people, places and things involved therein. Through a sociomaterial orientation to literacies and adoption of a modified Day‐in‐the‐Life methodology, this paper reports on the everyday literacies of ‘Gina’, an octogenarian woman who resided in an assisted living residence in the United States and self‐identified as thriving. The study identified six key literacies‐as‐events in Gina's day that engaged a plethora of (im)material constituents such as memories, art materials and novels and created opportunities for the (re)invention of time and space. Lessons from Gina's day suggest what might be possible in/through literacies at all ages.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".