Addressing Cross-National Generalizability in Educational Impact Evaluation
Bibliographic record
Abstract
Evaluation of educational programs has accelerated dramatically in the past quarter century. With this expansion has come clear methodological improvement involving randomized control studies and other approaches for establishing causation that considerably strengthen their internal validity. Such studies are, however, conducted within individual countries with the institutional structure of the schools and the national labor markets, and they are seldom replicated either within or across countries. A natural question is whether the results of an individual high-quality educational evaluation in one country can be reasonably applied in other countries. This paper focuses on existing research into differences across countries that, while generally impossible to incorporate into program evaluations, potentially have direct effects on key elements of policy and on the outcomes that can be expected. In particular, available cross-national studies on a variety of topics suggest using caution when generalizing evaluation results across countries, because student results are likely to vary systematically with a number of fundamental country-level institutional characteristics that are not explicitly considered in within-country evaluation analyses. Unfortunately, there is currently too little replication of basic research studies to provide explicit guidance on when and where cross-national generalizations are possible.
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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 | 0.731 | 0.853 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".