Globalization, statist political economy, and unsuccessful education reform in South Korea, 1993-2003.
Bibliographic record
Abstract
This article examines the relationship between globalization and national education reforms, especially those of educational systems. Instead of exploring the much debated issues of how globalization affects national educational systems and how the nations react by what kinds of systemic education reform, however, it focuses on what such a method often leaves out, viz., the internal conditions of a nation that facilitates or hampers reform efforts. Taking South Korea as an example, it explores that country's unique national context which restricts and even inhibits education reforms. Especially noted here is the established "statist" political economy in education. In the paper's analysis, although South Korea's statist political economy has made a substantial contribution to economic and educational development, it is now considered increasingly unviable as globalization progresses. Nevertheless, the internal conditions, resultant from the previous statist policies, set limits on policy makers' efforts to alter the existing educational system. The analysis suggests that a fuller assessment of globalization's impact upon national educational systems or their reforms requires a perspective which is broad enough to encompass not only the concepts and/or theories of globalization and nation states but also the power relations and ideological setup of individual nations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".