Пути оптимизации формирования эстетической культуры будущих хореографов
Bibliographic record
Abstract
1. Рєпнова Т. П. Тренінг емоційної зрілості / Т. П. Рєпнова // Практична психологія та соціальна робота. – 2006. – № 3. – С. 14-16. 2. Петько Л.В. Виховний і професійний аспекти музично-педагогічної спрямованості навчання іноземної мови студентів ВНЗ у системі музично-педагогічної освіти / Л.В.Петько // Музика та освіта ; гол.ред. Л.М.Масол. – Київ : Педагогічна думка, 2013. – № 3. – C. 14–18. URI http://enpuir.npu.edu.ua/handle/123456789/7872 3. Тернопільська В.І. Система виховання соціально-комунікативної культури учнів загальноосвітньої школи у позаурочній діяльності: дис. … доктора пед. наук : 13.00.07 / Тернопільська Валентина Іванівна. – К., 2009. – 573 с. 4. Тернопільська В.І. Соціально-комунікативна культура школяра: шляхи сходження : монографія / В.І. Тернопільська. – Житомир : Вид-во ПП “Рута”, 2008. – 300 с. 5. Pet’ko L.V. Formation of professionally oriented foreign language teaching environment in the conditions of university for students of art specialties / L.V.Pet’ko // Economics, management, law: problems of establishing and transformation: Collection of scientific articles. Psychology. Pedagogy and Education. – Al-Ghurair Printing & Publishing LLC, Dubai, UAE, 2016. – P. 395−398. URI http://enpuir.npu.edu.ua/handle/123456789/9779 6. Pet’ko Lyudmila. Isadora Duncan and Sergey Esenin // Lyudmila Pet’ko, Yevgenia Shpota // Intellectual Archive, 2014. – January. – Volume 3. – Number 1. – Toronto : Shiny Word Corp. – Р. 82–88. URI http://enpuir.npu.edu.ua/handle/123456789/7844
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.032 |
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".