Transformative Learning as a Factor of Lifelong Learning by the Example of Vocational Education in Canada
Bibliographic record
Abstract
Abstract The characteristics of transformative learning as a factor of life-long learning have been presented in the article. The paper offers analysis of foreign theorists and practitioners’ views on transformative learning at Canadian universities. A special attention has been paid to the exploration of transformative learning methods and techniques implemented during vocational training at universities. The analysis of theoretical background evidences that the transformative learning concept is based on the theory of person’s transformations depending on the life experience, cognitive development and critical reflection skills. The significance of transformative learning concepts implementation into Ukrainian educational process has been substantiated. The main principles of transformative learning have been described (education, science and manufacture integration, selfrealization through values and assumption transformation, focus on dialogue and critical self-reflection). The key elements of transformative learning have been determined, namely, disoriented dilemma, critical reflection and rational discourse. The importance of nonformal and non-linear educational techniques implementation has been proved.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".