Auditing governable space—A study of place‐based accountability in England
Bibliographic record
Abstract
Abstract The governance of territories has become increasingly fragmented and complex, challenging the accountability arrangements for “governable spaces.” Tension between central and local governments is a perennial feature of their relationship, but few analyses have explored the implications of this tension for accountability relationships. This article assesses policy initiatives within England aimed at increasing accountability in localities, by establishing governable spaces that include territorializing, mediating, adjudicating, and subjectivizing. During the 2010s, the UK government sought to introduce a form of place‐based accountability within the context of reduced central government funding to English local authorities. This meant that local government faced new forms of accountability while adapting to considerable financial shocks. Accounting methods—assessing what phenomena can and should be governed—underpin audit and orthodox concepts of accountability in the United Kingdom. These have driven a narrow finance‐focused narrative of local audit and local accountability. However, we also argue that developments in England in the 2010s have undermined political accountability in the localities, because they have worked against critical components within it for making governable space auditable: interpretation of data, judgments on service quality and the impact of cross‐public sector relationships on local authorities’ “decision space.”
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".