Narrative Gerontology: Countering the Master Narratives of Aging (Invited)
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.
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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 | 0.029 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| 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".