FOREIGNERS’ INTEGRATION AND LANGUAGE LEARNING IN ADULT EDUCATION IN CANADA: EXPERIENCE FOR UKRAINE
Bibliographic record
Abstract
The article deals with the research of special features of foreigners’ integration and language learning in Canada as an integral part of adult education. The paper outlines a complex of basic scientific approaches such as system, structural and functional, andragogical, axiological, intercultural, and competency-based approaches. The study analyses a number of programmes for immigrants’ integration in Canada, as well as language learning by adults. Critical overview allowed assuming that “Language Instructions for Newcomers to Canada” is the most popular programme among immigrants in this country. Taking into account the lack of similar programmes for foreign students in Ukraine and positive experience of Canada in this sphere, Lviv Polytechnic National University developed an elective course for international PhD students aimed at their adaptation in Ukraine. The article focuses on the features of the course which is based on the “Language Instruction for Newcomers to Canada” programme. Its content component is based on the demands, individual needs and involves the acquisition of language skills and acquaintance with the culture of the country. At the end of the course, the PhD students took part in a survey. The results of the survey showed that in spite of some difficulties, it appeared to be quite effective and informative.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".