On the supranational spell of PISA in policy
Bibliographic record
Abstract
Background: PISA results appear to have a large impact upon government policy. The phenomenon is growing, with more countries taking part in PISA testing and politicians pointing to PISA results as reasons for their reforms.Purpose: The aims of this research were to depict the policy reactions to PISA across a number of jurisdictions, to see whether they exhibited similar patterns and whether the same reforms were evident.Sources of evidence: We investigated policy and media reactions to the 2009 and 2012 PISA results in six cases: Canada, China (Shanghai), England, France, Norway and Switzerland. Cases were selected to contrast high-performing jurisdictions (Canada, China) with average performers (England, France, Norway and Switzerland). Countries that had already been well reported on in the literature were excluded (Finland, Germany). Design and methods: Policy documents, media reports and academic articles in English, French, Mandarin and Norwegian relating to each of the cases were critically evaluated.Results: A policy reaction of ‘scandalisation’ was evident in four of the six cases; a technique used to motivate change. Five of the six cases showed ‘standards-based reforms’ and two had reforms in line with the ‘ideal-governance’ model. However, these are categorisations: the actual reforms had significant differences across countries. There are chronological problems with the notion that PISA results were causal with regard to policy in some instances. Countries with similar PISA results responded with different policies, reflecting their differing cultural and historical education system trajectories.Conclusions: The connection between PISA results and policy is not always obvious. The supranational spell of PISA in policy is in the way that PISA results are used as a magic wand in political rhetoric, as though they conjure particular policy choices. This serves as a distraction from the ideological basis for reforms. The same PISA results could motivate a range of different policy solutions.
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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 | 0.051 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| 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; 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".