Feasibility of CLIL Implementation in the Education System of Azerbaijan: Attitude towards CLIL and the English Language in Higher Educational Institutions
Bibliographic record
Abstract
This article examines the feasibility of introducing Content and Language Integrated Learning (CLIL) in the education system of Azerbaijan, and determining the most preferred target language for this matter. Azerbaijan is a country with traditions of multilingualism. There are certain challenges within the society in terms of ensuring communication among different linguistic groups, which necessitate examining appropriate language policy and acquisition solutions. CLIL, which enables individuals to learn more than two languages at relatively high level, seems to be a promising approach for overcoming the linguistic communication problems in Azerbaijan, and enabling to take socio-economic and cognitive advantages of multilingualism. Whereas the question of feasibility of certain language acquisition approach in a specific country requires studying several dimensions such as public attitude, economic aspects and legal framework, the focus of the empirical research of this article is directed to learning the attitude to CLIL, which is a relatively under researched field. In the research part, it is sought to answer questions whether CLIL is supported among students in Azerbaijan, and which foreign language is preferred most. Moreover, it was sought to examine the relationship among the variables of gender, social class, existing language skills, as well as attitudes to CLIL and target language preference. It was found that CLIL is supported by participants, and English is the most preferred foreign language. It indicates that multilingual societies like Azerbaijan are supportive of language acquisition choices that help preserve and develop multilingualism. Moreover, it was found that attitude to language acquisition method and the language itself differ due to income level and existing language skills.
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 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".