Bibliographic record
Abstract
During the last few years, higher education has been the subject of constant transformation, innovations and reforms. In response to the new demands of the society, universities increase academic mobility, attract international students, introduce information technologies and new approaches to learning: personalized learning, problem-based learning, flipped classroom, distance learning, blended learning and so on. The role of a teacher in introducing new educational technologies and preparing the future generation is of great importance, so the main priority all over the world is to improve the quality of teacher education. The purpose of this paper is to analyze the current state of initial teacher training in Canada, a country where a larger proportion of students performed at the highest levels of proficiency according to the most recent PISA study, which indicates the quality of teacher training. The paper discusses the main features of Canadian teacher education programs: the teacher education models, the program structure and duration, the program content as well as different methods for assessing student learning outcomes with particular attention paid to modern approaches to assessing the student professional competencies at Quebec teacher faculties. Based on a literature review, the results of comparative studies of Canadian researches as well as universities regulations and official websites information, we analyze the main trends in the teacher education in Canada.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.021 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".