Preventing school exclusions of Black children in England – a critical review of prevention strategies and interventions
Bibliographic record
Abstract
This paper explores the literature on the prevention of exclusions of Black children in English schools which has remained an entrenched problem and persistent concern for many decades. It examines grey literature from projects, as well as tested approaches, and the impact of preventative strategies, identifying patterns of when and where Black pupils are most excluded. This review begins by exploring the combination of systemic and policy changes that may have contributed to increased exclusion levels and triangulates evidence from reviews and academic analysis from experts in the field. The paper then explores projects that have responded to increases in the exclusion of Black girls and presents evidence of the experiences of intersecting identities and discrimination, such as adultification, and how this has been found to contribute to growing disproportionate numbers of exclusions for girls. Qualitative data from multiple Ofsted and DfE reports are reviewed and the effects of using role models, as well as the roles that teachers and leaders play in reducing exclusions as key systemic apparatus. The paper ends with research on different types of interventions to prevent school exclusion and their varied successes.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".