Bibliographic record
Abstract
For a quarter century, American education researchers have tended to favour qualitative and descriptive analyses over quantitative studies using random assignment or featuring credible quasi-experimental research designs. This has now changed. In 2002 and 2003, the US Department of Education funded a dozen randomized trials to evaluate the efficacy of pre-school programmes, up from one in 2000. In this essay, I explore the intellectual and legislative roots of this change, beginning with the story of how contemporary education research fell out of step with other social sciences. I then use a study in which low-achieving high-school students were randomly offered incentives to learn to show how recent developments in research methods answer ethical and practical objections to the use of random assignment for research on schools. Finally, I offer a few cautionary notes based on results from the recent effort to cut class size in California.
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 | 0.122 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.014 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".