Prioritizing Improvement Among Disadvantaged Students in Principle and in Practice
Bibliographic record
Abstract
The US uses an evidence-based approach to education (US-EBE) as a strategy for pursuing two major goals: (1) to raise achievement in the US overall by facilitating improvement among all students, including students in disadvantaged groups; (2) to narrow achievement gaps between socially advantaged and disadvantaged groups by levelling up achievement among disadvantaged students. While both goals prioritize improvement among disadvantaged students in absolute terms, only the second attempts to address unequal achievement by prioritizing improvement among disadvantaged students relative to advantaged students. I argue that US-EBE can be reasonably expected to advance either the first goal or the second goal but not both simultaneously, as intended. This descriptive point raises a normative question: which goal should we pursue using US-EBE? I explore moral considerations that bear on this question, focusing on costs and benefits for students. I argue, provisionally, that we ought to use US-EBE to narrow gaps because the costs associated with doing so are morally justifiable whereas those associated with the alternative are not.
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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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".