Myron Korduba: A Legacy of Ukrainian Geography
Bibliographic record
Abstract
This article examines the life and research activity of the distinguished Ukrainian scholar in the field of geography Myron Korduba (1876–1947). His contributions to Ukrainian geography are revealed through a survey of his work in the areas of the population geography of Ukraine; econo- and politico-geographical regional and country studies; geographical pedagogy and cartography; and historical and toponymic geography. Korduba’s geographical legacy has not been widely investigated and is not well known in Ukrainian and international scholarship and education. This is mainly owing to the fact that he is perceived primarily a historian—indeed, one who enriched Ukrainian scholarship with innovative production in the historical disciplines. Thus, those who have studied Korduba’s creative output have tended to overlook his extraordinarily significant geographical oeuvre; it has heretofore received only superficial contemporaneous and contemporary exploration. Korduba’s principles and postulations in geography are introduced into the geographical scholarly literature in a novel way. They include ideas on the space, territory, and population of Ukraine; on the Ukrainian people, state, and language; on the treatment of geographical names as a historical source; and on the gleaning of information from the names of settlements.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".