Traveling Reforms from a Comparative, Transnational, and Global Perspective
Bibliographic record
Abstract
Abstract This chapter traces school reform trends over the period 1960–2025. It illustrates how the current reform began to take shape in the 1990s. It began first with a bundle of policies related to school-based management, choice, and privatization (summarized as school autonomy). A decade later, due to the detrimental effects of the neoliberal reform on equity and quality, a second bundle of policies was added that introduced standardized curricula, tests, and other performance evaluations (summarized as accountability). In the final stage, a full-fledged reform package circulated worldwide as a global script, featuring a particular theory of change, preferred policy instruments to induce and sustain change, and a specific mix of policies. Verger and his associates (Autonomous University of Barcelona) labeled the script School-Autonomy-with-Accountability (SAWA). The macroscopic preoccupation with international trends, presented in this chapter, has greatly benefited from research conducted by Patricia Bromley (Stanford University), Rie Kijima (University of Toronto), and their associates. They categorized and summarized over 10,000 policy documents that international organizations collected from almost every country and made publicly available in the World Education Reform Database. They convincingly show that the peak of the neoliberal era was 1992–2008.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".