Automating adult social care in the UK: Extracting value from a crisis
Bibliographic record
Abstract
The UK appears fixed in perpetual care crisis propelled by austerity-driven funding cuts and the country’s intensifying care labour shortage. Austerity in the UK has generated an operating environment that has accelerated privatisation and incentivized and necessitated private innovation. In this writing, we examine how the infrastructure and delivery of social care in the UK is being radically reimagined through technology. Promising to deliver critical efficiencies and cost savings, new automating technologies are being tested and introduced to reorder how and where care is managed and delivered. Drawing on a series of interviews conducted with local authorities in the UK and the executives of private companies, our task is not to assess the efficacy of new technologies; rather we examine how these public-private partnerships raise difficult questions: Who will deliver caring futures in the UK? Who is absorbing the fiscal risk of technological innovation? Who is profiting? And what are the limits of technology as a response to our care crisis?
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".