School Governing Bodies in England Under Pressure: The Effects of Socio-economic Context and School Performance
Bibliographic record
Abstract
This article reports research into the nature and functioning of school governing bodies in different socio-economic and performance contexts. The research analysed 5000 responses from a national questionnaire-based survey and undertook 30 case studies of school governing. The research confirmed that school governing in England is a complex and onerous responsibility that places governing bodies under considerable pressure. The socio-economic and performance contexts can be particularly demanding additional pressures. Governing bodies interact with those contexts in a complex way which we explain using the notions of governance capital and governance agency. Governance capital is the network of individuals and their capabilities, relationships and motivations that are available for the governing of a school. It is likely to be greater for schools that: are well regarded; are in high socio-economic status settings; and have high levels of pupil attainment. These effects may add and mutually reinforce creating an ‘amplifier effect’, which may significantly impact on the governing of a school. Governance agency is the capacity of those involved in the governing of a school to act. It is significant; can ameliorate the effects of low governance capital; and complicates the relationship between governing, performance and socio-economic context.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".