Dealing fairly with winners and losers in school: Reframing how to think about equality of educational opportunity 50 years after the Coleman Report
Bibliographic record
Abstract
Although the policy and methodological legacy of Equality of Educational Opportunity, the so-called Coleman Report published by the US Department of Education in 1966, is widely recognized, the way in which it played a role in shaping theorizing about equality of educational opportunity has been less well-explored. This article reconsiders the Coleman Report in light of how it has contributed to the framing of how we think about the very idea of equality of educational opportunity and its normative capacity to evaluate the education system. The main argument is that the Coleman Report helped to crystallize the concept of equality of educational opportunity as being fundamentally about how education provides ladders of opportunity and enables upward mobility for socially disadvantaged students. This framing has, however, created certain normative blind spots that have seriously impeded its ability to engage certain issues in education. The normative blind spot singled out, in particular, is the competitive structure of the current education system – the school system is structured so that some children win, while some lose. The competitive nature of schools has often been challenged, from a wide range of perspectives, but rarely utilizing an equality of opportunity framework. Given this normative blind spot, the more constructive dimension of the article is to advance an alternative theory of equality of educational opportunity that is better able to function as a regulative ideal for competition in the education system, one that identifies standards or principles of fairness to guide policy on how to deal fairly with winners and losers.
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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 | 0.088 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.023 | 0.130 |
| Scholarly communication | 0.032 | 0.046 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.022 | 0.038 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".