Bibliographic record
Abstract
In Chapter 5 we started a process of thinking through divergent practices, those of families and formal care providers, as well as some of the kinds of relations family practices must make with ‘other’ practices. In thinking through this question of relations between practices, it is impossible not to see, and discuss, the effects of the ‘patterning’ of dementia in family practices. In this chapter we focus more specifically on this idea of patterning, exploring how each family needed to make relations with the dominant dementia discourse – specifically, the changes each needed to make to remain ‘in sync’ with the idea of the ‘dementia trajectory’. As has been well established in multiple disciplines, medicine serves a powerful organizing function in people's experiences of health and illness (see, for example, Armstrong, 1982; Gubrium, 1986; Cohen, 1998; Dillman, 2000; Beard, 2016), with the biomedical gaze an exemplar of a way of seeing that looks for patterns, that systematizes in order to know and to intervene (Foucault, 2003). Identifying and ordering ‘disorder’ through the concept of disease, biomedical discourses and practices also direct how such disorder should be perceived and acted on (Dillman, 2000; Holstein, 2000). An important element of this influence is, as Foucault points out, the anteriority of the medical gaze: ‘one now sees the visible only because one knows the language’ (2003, p 140). The analytical structure, he suggests, precedes the picture, providing knowledge ‘not of what “is” but of the anteriority of ordering’ (2003, p 140). Thus the perceived ‘ “givenness” of the disease model’ itself (Holstein, 2000, p 171), its easy recognition and mostly smooth application, becomes important in our current context where Alzheimer's disease and other dementias have become the dominant medicalization of old age (Cohen, 1998; Lock, 2013; Latimer, 2018), and where the phenomenon of ageing itself has become deeply associated with a ‘crisis rhetoric’ (Beard, 2016, p 5). As discussed in previous chapters, fears of demographic ageing, connected explicitly to predictions of an increasing incidence of dementia and the grave threats this is thought to pose to the future sustainability of health and social care systems, mobilize research and policy and shape public attitudes and knowledge (Holstein, 2000; Lock, 2013; Latimer, 2018).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".