Seeing Near and Far: Balancing Stakeholder Needs and Rights in Kazakhstan’s Educational Reform
Bibliographic record
Abstract
This paper examines the schooling reform in the post-socialist transformation of Kazakhstan. Adopting a rights-based approach to education, it looks at the ways in which the current education system addresses (or fails to address) the rights and needs of various stakeholders in the society, including teachers, learners, parents, civil society, and policymakers. Two recent large-scale educational reforms form the focus of the paper: a national standardized assessment and a transition from 11 to 12 years of schooling. Implications of the current reform initiatives for Kazakhstan’s development are also discussed, pointing to lessons for understanding schooling and social change in post-socialist transformation. Cet article examine la réforme scolaire pendant la période de transformation post-socialiste du Kazakhstan. En adoptant une approche basée sur les droits de l'éducation, l'auteur considère les méthodes que l'actuel système d'éducation emploie pour répondre (ou ne pas répondre) aux droits et aux besoins des parties prenantes de la société, y compris, les enseignants, les apprenants, les parents, la société civile, et les responsables de politique. Cet article met au point le deux récentes réformes scolaires à grande envergure: la standardisation de l'évaluation nationale et l'ajout d'une année en plus à l'ancien système scolaire de 11 années. L'auteur y examine aussi les répercussions des initiatives de la réforme actuelle pour le développement du Kazakhstan, et suggère les leçons qui pourront nous faire comprendre l'éducation et le changement de la société dans la transformation post-socialiste.
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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 | 0.065 | 0.052 |
| 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 | 0.024 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.004 |
| 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".