An Analysis of School-Based Contextual Indicators for Possible Use in Widening Participation
Bibliographic record
Abstract
This paper looks at the National Pupil Database for England in terms of variables that could be used by universities to help them assess undergraduate applications. Where a young person is obviously disadvantaged, this can be taken into account in contextualised admissions. Of the indicators available, which give the most accurate assessment of that context—singly or in combination? This paper looks at missing data, and what is known about students for whom data is missing. It looks at changes in indicators of potential disadvantage over time. And it looks at the relationship between all indicators and student attainment and progress at school. The safest and clearest indicators are the sex (male) and age in year (summer born) of a student but neither of these is currently considered in widening participation. Otherwise, the best general indicator is eligibility for free school meals (poverty), and this is best computed as the number of years a student has been known to be eligible. Having a special educational need is also a promising indicator, but doubts are raised about its validity and it anyway covers a wide range of factors, some of which are already dealt with by the education system. Very few students registered as living in care continue in education post-16, and this indicator could be used safely and to advantage. The rest, including area measures, school type, performance relative to school, ethnicity and first language are generally not safe to use.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".