Narrative Gerontology: Countering the Master Narratives of Aging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Narrative approaches to understanding later life are increasingly being used within gerontology, albeit in limited ways. These limits include the number and types of narratives that “count” as knowledge or data as well as narrowly applied methods for analysis and interpretation. Within the gerontology field, the overriding assumption is still one that presumes that the stories we tell are the stories we are. Still missing are critical questions of whether dominant narrative approaches in the field truly give voice to the experience or instead perpetuate master narratives of later life. If so, what counter narratives are available? For example, in oral interviews, there is often little consideration given to the context in which the narratives unfold. In written narratives, the almost exclusive use of the first-person memoir format shapes what stories are voiced and which are silenced. In this paper, I draw from my own research within narrative gerontology to illustrate some of the challenges with how narratives are elicited, analyzed, and interpreted within the field in both oral and written approaches and suggest directions for future narrative work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it