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Record W4309323945 · doi:10.1177/10567879221137572

Global Competitiveness Myths and Ideals: English Language Policy in Universities in Kazakhstan

2022· article· en· W4309323945 on OpenAlexaff
Seth A. Agbo, Natalya Pak, Azamat Akbarov, Gulmira Madiyeva, Yerbolat Saurykov

Bibliographic record

VenueInternational Journal of Educational Reform · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsLakehead University
Fundersnot available
KeywordsKazakhNational identityPoliticsPolitical scienceForeign languagePedagogyLanguage policyGovernment (linguistics)Higher educationMalaySociologyForeign policyPublic relationsEconomic growthLinguisticsEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.347
Teacher spread0.340 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations13
Published2022
Admission routes1
Has abstractyes

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