Women, Men and Social Class Revisited: An Assessment of the Utility of a `Combined' Schema in the Context of Minority Ethnic Educational Achievement in Britain
Why this work is in the frame
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Bibliographic record
Abstract
The last quarter of the 20th century gave rise to debate in this journal and elsewhere regarding the treatment of women in class analysis. It is argued here that the question of minority ethnic achievement has given new impetus to arguments in favour of taking account of mother's occupation in class schemas.The article constructs three different class schemas and tests their utility in this context. It then uses one schema to assess the importance of social class in explaining achievement differentials among minority ethnic pupils in Britain. Class background is found to be a key factor for all groups.The analysis finds significant differences between ethnic groups even when pupils from the same social class background are compared. When disparities within ethnic groups are examined, however, it is found that the effect of moving one place down the social class structure is similar.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it