The socio-economic gradient in educational attainment and labour market outcomes: A cross-national comparison
Bibliographic record
Abstract
This paper reviews evidence on the link between family background, \neducational attainment and labour market outcomes across four rich \nEnglish-speaking countries (Australia, Canada, England and the United \nStates of America). It uses a life course approach, where the magnitude \nof socio-economic disparities is measured and compared cross-nationally \nat key transition points. We find that socio-economic inequalities are \nusually (although not always) smallest in Canada and greatest in the USA. \nThus, drawing upon evidence from a collection of independent studies, \nwe find little evidence to support suggestions that the USA is the land of \nopportunity, where individuals from humble origins can successfully pursue \nthe American dream. Rather, family background matters more to lifetime \nopportunities in the USA than in other comparable countries.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".