PISA Country Rankings Valid? Results for Canada and Finland
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This article examines whether the way that PISA models item outcomes in mathematics affects the validity of its country rankings. As an alternative to PISA methodology a two-parameter model is applied to PISA mathematics item data from Canada and Finland for the year 2012. In the estimation procedure item difficulty and dispersion parameters are allowed to differ across the two countries and samples are restricted to respondents who actually answered items in a mathematics cluster. Different normalizations for identifying the distribution parameters are also considered. The choice of normalization is shown to be crucial in guaranteeing certain invariance properties required by item response models. The ability scores obtained from the methods employed here are significantly higher for Finland, in sharp contrast to PISA results, which gave both countries very similar ranks in mathematics.
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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 | 0.022 | 0.334 |
| 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 | 0.000 |
| Open science | 0.001 | 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 it