EXTENDING GERONTOLOGICAL KNOWLEDGE AND THEORY THROUGH ETHNOGRAPHIC CASE STUDY METHODS
Bibliographic record
Abstract
Abstract Experiences of older people deemed to be ‘at risk or ‘in need’ of intervention are often viewed through professional classifications with corresponding ‘objective’ indices. Concepts such as frailty and mobility, which have come to draw international policy attention, and arguably shape understandings of aging, are also produced within disciplinary knowledge(s) and powerful practices that prioritize particular components over others. As critical perspectives have revealed, such processes can marginalize groups of older people, resulting in research that articulates counter positions based on disjuncture between classifications, responses, and experience. Yet, operating in the inter-disciplinary, bio-medical/technocratic, and applied contexts of gerontology, research findings themselves become sets of knowledge and discourse which are understood as binaries, with the associated sociological theoretical perspectives often marginalized in the process. This paper suggests that ethnographic case study methods can be used to better understand the social complexities of aging, including the framing and interpretation of experience(s), the everyday contexts where older people negotiate and enact relationships and lives over time, and the ways in which evidence is used to design and respond to (or deny) older people’s needs. It takes a critical position focused on the production of knowledge and the experiences of older people in the context of social, cultural, and political relations, arguing for methods which render visible the complex realities of aging that are constructed, experienced and lived through, in space and time. It outlines the ethnographic case study as one potential method to carry out this work, presenting examples on frailty and (im)mobility.
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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 | 0.103 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".