Addressing Cross-National Generalizability in Educational Impact Evaluation
Bibliographic record
Abstract
The evaluation of educational programs has accelerated dramatically in the past quarter century.While such evaluations were once almost exclusively conducted in the U.S., they have broadened dramatically across many countries of the world.At the same time, the methodology has improved, strengthening considerably the internal validity of various studies.We must now consider what conclusions can be drawn from the growing wealth of international results.In particular, available cross-national studies on a variety of topics suggest using caution when generalizing results, because the results vary systematically with a number of institutional characteristics of the different countries that are not explicitly considered in within-country analyses.
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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 | 0.627 | 0.821 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".