Equality, adequacy, and stakes fairness: Retrieving the equal opportunities in education approach
Bibliographic record
Abstract
Two approaches to making judgments about moral urgency in educational policy have prevailed in American law and public policy. One approach holds that educational policy should aspire to realizing equal opportunities in education for all. The other approach holds that educational policy should aspire to realizing adequate opportunities in education for all. Although the former has deep roots in American culture and its jurisprudence, a common narrative is that in recent years the equal opportunities approach has been displaced by the educational adequacy approach, which is said both to have enjoyed much greater success in the school financing litigation as well as to be theoretically more defensible. The present article is designed to make a contribution to the retrieval of the equal opportunities approach. It does so by sketching out a theory of equal opportunities in education organized around the idea of stakes fairness that can withstand the criticisms often made of that approach and by showing how that theory is better able than the educational adequacy approach to address the fairness of a more robust educational policy agenda that extends beyond school financing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.059 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".