How do you Shape a Market? Explaining Local State Practices in Adult Social Care
Bibliographic record
Abstract
Abstract The Care Act 2014 gave English local authorities a duty to ‘shape’ social care markets and encouraged them to work co-productively with stakeholders. Grid-group cultural theory is used here to explain how local authorities have undertaken market shaping, based on a four-part typology of rules and relationships. The four types are: procurement (strong rules, weak relationships); managed market (strong rules, strong relationships); open market (weak rules, weak relationships); and partnership (weak rules, strong relationships). Qualitative data from English local authorities show that they are using different types of market shaping in different parts of the care market (e.g. residential vs home care), and shifting types over time. Challenges to the sustainability of the care system (rising demand, funding cuts, workforce shortages) are pulling local authorities towards the two ‘strong rules’ approaches which run against the co-productive thrust of the Care Act. Some local authorities are experimenting with hybrids of the two ‘weak rules’ approaches but the rival cultural biases of different types mean that hybrid approaches risk antagonising providers and further unsettling an unstable market.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".