Development of Pedagogical Schools and Colleges in Ukraine in the 1990s – First Quarter of the 21st Century: Historiography of the Problem
Bibliographic record
Abstract
The article provides a historiographical analysis of scientific sources on the development of pedagogical schools and colleges in Ukraine from the 1990s to the first quarter of the 21st century to assess the current state of research and the prospects for further study. To address this task, a two-vector classification of scientific sources was developed. «Vertically», based on type and informative-content criteria, they are divided into four groups: 1) generalizing works; 2) monographs and dissertations; 3) scientific articles and materials from scientific conferences; 4) reference publications. Their informative-cognitive potential and features are characterized. «Horizontally», based on content-thematic criteria, six subgroups of historiographical sources are identified and characterized: 1) scientific-theoretical works that provide the foundation for developing a methodology for studying pedagogical schools and colleges; 2) studies on the development of pedagogical colleges abroad; 3) works that illuminate the historical retrospective of the development of these educational institutions in Ukraine; 4) research related to various aspects of the development of the modern system of higher and professional-pedagogical education in Ukraine (stages, continuity, etc.); 5) the fundamental subgroup of works that directly highlight the activities and development of pedagogical schools and colleges in Ukraine; 6) works on the functioning of pedagogical colleges under the conditions of the Russian-Ukrainian war.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".