CLIL METHODOLOGY IN TEACHING ACADEMIC WRITING AND INTEGRITY
Bibliographic record
Abstract
The article provides an overview of CLIL methodology as a contemporary approach to teaching non-language subjects in an additional (foreign) language.Having its roots in the French immersion programs and bilingual education in Canada and the USA in the 1950s, CLIL has been gaining popularity in Europe in the last decade.Ukraine has also become one of the countries where CLIL methodology is being actively implemented at different educational levels.CLIL differs from ESP in that the latter aims at forming those foreign language skills which are required from future professionals in the professional environment while CLIL has a dual focus on the content and language.The theoretical framework of CLIL is constituted by 4Cs: content, communication, cognition, and culture.The interrelation of these four principles is supposed to ensure the balanced acquisition of a subject and a foreign language.Researchers differentiate between two models of CLIL -'soft' and 'hard'.'Soft' CLIL is language-focused while 'hard' CLIL is subject/contentfocused.Between the two ends of the 'soft-hard' continuum there can exist multiple versions of CLIL when teachers select a necessary balance of content and language with regard to the students´ capabilities and needs.CLIL implies the use of only authentic materials (e.g., textbooks and videos which are intended for native speakers and can represent real life situations).Another important idea behind CLIL is scaffolding -supporting students at all the stages of studying.Scaffolding aims to compensate for the lack of verbal explanation which sometimes can be too complicated and be at variance with the students´ language competence.Scaffolding can be verbal (vocabulary of the subject) and non-verbal (colours, gestures, pictures, movements, sounds, etc.) with one complementing another.In this paper we provide examples of applying CLIL methodology while teaching academic writing and integrity.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".