SOCIAL AND CULTURAL FACTORS OF PRIMARY SCHOOL TEACHER TRAINING IN CANADA (1950–1990S)
Bibliographic record
Abstract
The article analyzes the demographic and economic ties and describes the social and cultural factors that in Canada in the 1950 -1990s determined changes in the tasks, organization, and training of teachers to work in primary school, taking into account the cultural needs of the population and government education policies. The application of historical and genetic as well as comparative methods of documentary sources analysis made it possible to identify the social and cultural-regional conditionality of the tasks and content of teacher training and their preparation for work in primary schools in different provinces. The article highlights the main contexts according to which the professional training of Canadian primary school teachers was carried out: historical, multicultural, traditional cultural, pedagogical, and religious. As a result of the comparative analysis of historical development, it was found that in Canadian cities such as Montreal, Toronto, and Vancouver there were concentrated large settlements of migrants, which played a dominant role in social and cultural development of Canada. Two main vectors of teacher training, multicultural and cross-cultural, which met the requirements of Canadian social environments and educational policy of Canadian governments, are studied. The training programs for primary school teachers in the provinces of Quebec, Ontario, and British Columbia, initiated mainly by the federal government of Canada, are described. The Government of Canada, together with the Ministries of Education, colleges and universities, has been found to have influenced the training of Canadian primary school teachers by creating a variety of educational programs best suited for the needs of society.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".