The Analysis Of Adult Immigrants’ Learning System In Canada
Bibliographic record
Abstract
Abstract In the article the problem of adult immigrants’ learning in Canada has been studied. The main objectives of the article are defined as: analysis of scientific and pedagogical literature which highlights different aspects of the research problem; analysis of the adult immigrants’ learning system in Canada; and the perspectives for creative implementation of Canadian experience in Ukraine. Adult education and learning throughout the world have been studied by foreign and domestic scientists: fundamentals of lifelong education (O. Martirosyan), theory and practice of adult education (V. Horshkova); peculiarities of adult learning (L. Mazurenko); andragogical (M. Knowles), structural and functional, systemic approaches (N. Alboim); personality-oriented (S. Lisova); axiological (T. Brazhe) approaches; psychological, pedagogical, andragogical, sociological researches of adult education (T. Kuchay, L. Tymchuk) etc. Adult education in Canada has been studied by M. Borysova, N. Mukan, O. Ohiyenko, but the learning system of adult immigrants has not been studied yet. Among research methods we have used comparative and logical methods, induction and deduction, content analysis, prognostic method etc. The following research results have been presented: the adult immigrants’ learning has been described as a system which consists of such components as the aim and objectives, fields of study, functions, principles, legal framework, environment and stages of learning, content and operational components, monitoring and assessment. Among the perspectives of further research we can define the analysis of Canadian “Prior Learning Assessment and Recognition” system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".