Policy Gymnastics: the case of Multi-Academy Trusts
Bibliographic record
Abstract
The Academies Act was passed in 2010 by the newly elected Conservative-Liberal Democrat Coalition Government. It made provision for Local Authority (LA) maintained schools in England to convert to academies, which are funded and overseen by national (rather than local) government. 14 years later, the landscape is transformed: two in five primary schools and four in five secondary schools are now academies. However, whereas academies were originally positioned as highly autonomous, with additional ‘freedoms’ compared with other schools, most academies have now been subsumed into a Multi-Academy Trust (MAT), meaning they cease to exist as a separate legal entity. This chapter examines the evolution of policy on academies and MATs. It argues that policy makers have engaged in policy gymnastics as they have sought to evolve the academy reforms in ways which address legitimacy concerns and offer scope for efficiency and effectiveness. These gymnastics have involved strategic, linguistic and regulatory contortions, often driven by competing values and logics. Drawing on Stewart’s (2006) work we argue that these contortions have relied on four mechanisms: establishing a new policy paradigm; technicisation; cycling and structural separation. These policy-level gymnastics have impacted on front line leaders, who have needed to continually stretch and flex in order to lead schools and educate children even as the system has contorted around them.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.025 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 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".