Public Service Mutuals: Spinning out or standing still?
Bibliographic record
Abstract
Richard Hazenberg and Kelly Hall from the University of Northampton and Allison Ogden-Newton, Chair of the Transition Institute, consider how a more nuanced discussion of where, and under what conditions mutualisation brings social and financial value would be helpful. In his conclusion, Paul Buddery suggests that, just as the Enterprise Solutions project has itself seen a range of solutions, including but not limited to mutualisation, so the future of spin-outs is likely to take a number of different forms. Employee led mutuals may grow but so too will collaborative models, joint ventures, asset transfers and in house trading companies. As the appetite for spin-outs increases and new providers move into delivery, important opportunities arise for reviewing the evidence. It is important to assess the extent to which social enterprises and mutuals will or will not be able to effectively involve service users and deliver more efficient, responsive and high quality services than the public sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.014 | 0.029 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 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".