Collective Choice and Individual Action: Education Policy and Social Mobility in England
Bibliographic record
Abstract
It is recognised that expressive preferences may play a major role in determining voting decisions because the low probability of being decisive in elections undermines standard instrumental reasoning. Expressive and instrumental preferences may deviate and in electoral settings it is more important to make policies expressively appealing. Policies are even more attractive if they can be made both expressively and instrumentally appealing. This paper studies education policy in England and proposes that arguments for increased state spending in school education is expressively appealing as it appears equitable, but the allocation of students to schools by catchment area is also instrumentally appealing to middle-class families. Allocation to schools by lottery may be expressively but not instrumentally appealing. Cutting education spending and dividing the proceeds between a tax cut to the affluent and a cash transfer to the poor may be instrumentally but not expressively appealing. The effort to provide instrumentally appealing policies with sufficient ethical content to satisfy expressive preferences may lead to inefficiency and distract attention from more serious ethical problems related to the policies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".