Curriculum reform in post‐Soviet Kyrgyzstan: indigenization of the history curriculum
Bibliographic record
Abstract
This article raises general questions regarding the relations between curriculum and ideology in reforming, specifically indigenizing the curriculum by focusing on the importance attached to the history curriculum under reform circumstances. Through an examination of indigenization of history curriculum in the context of Kyrgyzstan, a post‐Soviet republic in Central Asia, it offers a description and understanding of particular forces, interests and circumstances that surround the curriculum design process. It discusses indigenization as a political, social and cultural process, which emerges as a response to a long‐term domination, neglect and denigrtation by the colonial regimes and powerful groups of the culture, languages and traditions of the indigenous people. Based on the declonization experiences of other formerly colonized nations, the article explores the implications of indigenization of curriculum for learners. It also discusses the recent educational and pedagogical practices, which have emerged to address implications of indigenization for the curriculum users, resolve its limitations and to establish new, more inclusive visions for curriculum design. The analysis is constructed on the basis of the review of relevant theoretical literature, newspaper and journal articles, history textbooks and other learning materials used in schools and universities.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".