The Use of Local Lore Materials at Lessons of History of Ukraine in Secondary General Education Institutions
Bibliographic record
Abstract
The article studies the peculiarities of the use of local lore materials at history lessons. The author concludes that the use of local lore materials at history lessons is one of the means of enhancing students' cognitive activity and encouraging research. Knowledge of local lore studies enriches program material, makes it clearer, more logical and convincing and, of course, helps to improve the quality of knowledge. In the process of study of History of Ukraine, focusing on specific facts, events, processes and phenomena of local lore contributes to students' awareness of the historical basis of the regional-cultural, ethnographic, religious, social and economic diversity of Ukraine within a single national history. Understanding of regional peculiarities (historical-geographical, ethnographic, cultural, religious, mental) as a result of a complex of events, processes and phenomena of the historical development of the Ukrainian territory is a significant factor in shaping the social consciousness of the young generation, which has to understand the state and national unity of Ukraine through regional variety of all manifestations of life of the Ukrainian nation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".