The politicization of PISA in evidence-based policy discourses
Bibliographic record
Abstract
Education reform efforts stemming from the Programme in International Student Achievement have strengthened in recent years, particularly in response to the growth of global references societies – high achieving educational jurisdictions such as Finland, Hong Kong-China, and more recently Estonia and Singapore. Despite political rhetoric, evidence-based policy development associated with this international benchmark measure is rarely, if ever, a neutral enterprise that is guided by the best available evidence. Indeed, political discourse and policy framing surrounding PISA often results in the selective use of results to justify contested policy reforms. Brief cases from Japan, Sweden, and Canada illustrate how national policies have been adopted that are not grounded, and may even run counter, to research findings. The discussion examines the politicization of PISA and its symbolic role in adding legitimacy to education reform agendas. Collectively, the analysis offers an alternative perspective to the popular notion that PISA guides evidence-based decision-making.
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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 | 0.286 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.013 | 0.127 |
| Scholarly communication | 0.039 | 0.027 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.019 | 0.034 |
| Insufficient payload (model declined to judge) | 0.002 | 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".