Global Competitiveness Myths and Ideals: English Language Policy in Universities in Kazakhstan
Bibliographic record
Abstract
It seems Kazakhstan couches superior knowledge in one particular language. The government policy for educational change focuses on reaching some aspects of equivalence or parity with developed and advanced nations to the extent that they approximate the attributes of prestigious national societies such as the rich European and North American countries. Current government policy in Kazakhstan calls for a policy dubbed “trilingualism” which means proficiency in Kazakh, Russian, and English. This study utilized various qualitative methods such as interviews, participant and non-participant observation, and document analysis to investigate faculty, students, and administrators’ experiences of the change from teaching and learning in Russian and Kazakh to English. The findings indicated that for Kazakhstan's universities to become globally competitive, they must have clear-cut goals that directly manifest how language conveys society's essential values. The unpreparedness of students, faculty, and administrators delimits the changeover from Kazakh and Russian to English. Accordingly, among the essential criteria to foster the foundation of development are national identity, harmony between the educational system, and, most generally, the extent of political decision-making to meet the national society's educational needs.
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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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.023 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.001 | 0.009 |
| 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".