Primary and Secondary Effects in Class Differentials in Educational Attainment
Bibliographic record
Abstract
In this article we start from Boudon's important, but still surprisingly neglected, distinction between `primary' and `secondary' effects in the creation of class differentials in educational attainment. Primary effects are all those, whether of a genetic or socio-cultural kind, that are expressed via the association between children's class backgrounds and their actual levels of academic performance. Secondary effects are those that are expressed via the educational choices that children from differing class backgrounds make within the range of choice that their previous performance allows them. We apply a method introduced by Erikson and Jonsson to represent the relationship between primary and secondary effects in analysing class differentials in one crucial transition within the English and Welsh educational system: that which children make at around age 16 and which determines whether or not they will pursue the higher-level academic qualifications — A-levels — that are usually required for university entry. We then use a development of this method that we have earlier proposed in order to produce quantitative estimates of the relative importance of primary and secondary effects as they operate within this transition. We show that secondary effects reinforce primary effects to a substantial extent, accounting for at least one quarter, and possibly up to one-half, of class differentials as measured by odds ratios. In conclusion, we consider some theoretical and policy implications of our findings.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".