Personalisation and Austerity in the Crosshairs: Government Perspectives on the Remaking of Adult Social Care
Bibliographic record
Abstract
Abstract Personalisation has now become centre-stage in adult social care and continues to have an enduring level of political commitment and on-going appeal for many disabled people. And yet its roll-out has taken place during a time of austerity where central governments in many neo-liberal countries are re-imagining (read: shrinking) their role in social care provision. This paper reports on findings from an empirical study of relevant government officials from different countries which have advanced personalisation: Canada, England and the US. It reports on their views on personalisation and the remaking of adult social care, and managing expectations for change. Despite the relative success of personalisation, the findings reveal a tempered, cautious account, with respondents aware of the pitfalls and risks inherent in self-led support, government limitations in changing systems and an end to the primary involvement by the state in the creation of a social care market. With this in mind, the study's findings make a strong case for forms of ‘progressive localism’, as imagined by Featherstoneet al. (2012), in galvanising local community resources alongside more radical politics in order to make self-led support achieve its desired outcomes on the ground.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.043 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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 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".