Exploiting globalization while being exploited by it:Insights from post-Soviet education reforms in Central Asia
Bibliographic record
Abstract
Building on an examination of comparative and international literature and their research and development experiences, the authors highlight a number of continuities, changes, and issues between Soviet and post-Soviet, international and Central Asian experiences of borrowing and lending of education reforms. Even though Central Asian actors and institutions are not totally helpless victims and though international experts and NGOs appear well-meaning in these globalizing education transfers, the processes are leading toward reproducing global and local dependencies and inequalities.The trajectory of education reforms in Central Asia echo those of other developing countries. In response, the authors urge local policy makers and comparative educators to join in a critical and reflexive strategic venture of re-encountering and reshaping the global and neoliberal offers to serve the needs of interconnected local and global justice.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| 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".