Bibliographic record
Abstract
У статті проаналізовано праці українських авторів, присвячені життю та творчості видатного історика і громадського діяча. Наведено факти, що доводять помітну активізацію дослідження біографії видатного українця за останню чверть століття. Особливістю сучасного періоду розвитку багалієзнавства є яскраво виражена його археографічна складова, а саме публікація листування Д. І. Багалія, його рукописів і повторний друк найбільш відомих монографій історика. \n \nВ статье проанализированы работы украинских авторов, посвященные жизни и творчеству выдающегося историка и общественного деятеля. Приведенные факты доказывают заметную активизацию исследования биографии выдающегося украинца за последние четверть века. Особенностью современного периода развития багалеезнавства является ярко выраженная его археографическая составляющая, а именно публикация переписки Д. И. Багалея, его рукописей и повторная печать наиболее известных монографий историка. \n \nIn the article analyzed the works of Ukrainian authors devoted to the life and work of an outstanding historian and public figure. The facts prove a noticeable intensification of the study of the biography of a prominent Ukrainian during the last quarter century.The peculiarity of the modern period of the development of Bagalyі’s studies is its archeographic component, namely the publication of the correspondence D. Bagalyi, his manuscripts and the reprint of the most famous monographis of the historian.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.049 | 0.017 |
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".