The Model of Unification and the Model of Diversification of Public School Teachers’ Continuing Professional Development in Great Britain, Canada and the USA
Bibliographic record
Abstract
Abstract In the article the theoretical framework of public school teachers’ continuing professional development (CPD) in Great Britain, Canada and the USA has been presented. The main objectives have been defined as theoretical analysis of scientific and pedagogical literature, which highlights different aspects of the problem under research; presentation and characteristic of two models: the model of unification and the model of diversification of teachers’ professional development in the systems of continuing pedagogical education of Great Britain, Canada and the USA by the dominant traits. Their major components have been defined and specified. Public school teachers’ CPD has been studied by foreign and domestic scientists: content of public school teachers’ CPD (N. Dana Fichtman, M. Rees, A. Ross, S. Zepeda); CPD programs (C. Pratt); public school teachers’ CPD models, methods and forms (K. Duinlan, P. Grimmet, G. Troia, P. Wong); continuous professional education (Ya. Belmaz, А. Kuzminskyy, O. Kuznyetsova). The research methodology comprises theoretical (logical, induction and deduction, comparison and compatibility, structural and systematic, analysis and synthesis) and applied (observations, questioning and interviewing) methods. The research results have been presented.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".