Young peoples’ experiences of exclusion from school in the political economies of the four UK jurisdictions
Bibliographic record
Abstract
The data in this article were gathered as part of a larger study of school exclusion in the four jurisdictions of the United Kingdom (UK). Key points of investigation involved attempting to understand why there were disproportionately higher official school exclusions in England, in terms of temporary and permanent exclusions, compared to the rest of the UK and an exploration of informal or unofficial forms of exclusionary practices. Education in each jurisdiction of the UK (England, Wales, Scotland and Northern Ireland) is devolved and there are different legislative and policy arrangements in place. In this paper, we consider young peoples’ experiences of exclusion in what are ultimately different cultural contexts. We were minded that disability needs to be understood within the settings in which it is experienced and is thus cultural, contextual and fluid. Young people were interviewed, and vignettes illustrating experience were constructed and a coding frame was developed. We have presented the outcomes of the analysis organised by individual themes across jurisdictions. Our conclusions suggest that experience may not be as contextually labile as expected, rather that incidence was a more salient factor. Exclusion is not a pleasant experience although it is more common in some settings than others.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".