Social class and sex differences in absolute and relative educational attainment in England, Scotland and Wales since the middle of the twentieth century
Bibliographic record
Abstract
Changes over time in social-class inequality of educational attainment have been shown by previous research to depend on whether attainment is measured absolutely or relatively. The pioneering work in this respect by Bukodi and Goldthorpe found that inequality has fallen when attainment is measured absolutely (for example, as the percentage completing full secondary schooling) but has changed less when a relative measure is used (for example, reaching the top quarter of the distribution of attainment). Although absolute measures remain intrinsically interesting, insofar as they represent cognitive or cultural accomplishment, relative measures are more relevant for understanding the role of education in allocating people competitively to employment. Implicit in this previous research, as in much research on the connection between education and social mobility, is that the society over which the relative standing of qualifications is measured is the same as that in which they are used to gain social rewards, such as a job. When labour markets operate across educational borders, this assumption might be open to question. The present analysis investigates the interpretation of absolute and relative educational inequality by comparing England, Scotland and Wales, which have distinct education systems but a common labour market.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".