The conspicuous absence of the social, emotional and political aspects of frailty: the example of the<i>White Book on Frailty</i>
Bibliographic record
Abstract
Abstract Over the last 15 years, frailty has become a dominant discourse on late life. Taken-for-granted knowledge and practice can be seen in initiatives such as the International Association of Gerontology and Geriatrics’White Book on Frailty. This paper begins with an overview of key themes on frailty from the biomedical literature, followed by critical literature in the social sciences and humanities. It discusses the tensions within the biomedical field, frailty as a social construction and ‘social imaginary’, practices of frailty as historically linked to political systems of care, and frailty as an emotional and relational experience. It then draws on a critical discourse analysis to assess the 2016White Book on Frailty. Drawing on the idea of ‘significant absences’, the paper highlights the gaps that exist where the social and emotional understandings and political readings of frailty are concerned. The paper concludes by outlining the need to recognise the ‘politics of frailty’ including the power relations that are deeply embedded in the knowledge and practices surrounding frailty, and to incorporate older people's experience and ideas of vulnerability into research, policy and care practice.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".