Gathering a Heritage: Ukrainian, Slavonic, and Ethnic Canada and the USA
Bibliographic record
Abstract
Preface Acknowledgements Introduction EMIGRATION STUDIES 1. The Great Migration: East Central Europe to the Americas in the Literatures of the Slavs: Some Examples 2. A Little-known Book from the Late Soviet Period on the Economic Emigration from Imperial Russia to Western Europe and North America, 1880-1914 3. Ivan Franko and Large Scale Ukrainian Economic Emigration to Canada before 1914 4. A Polish Scholar on Polyethnic Emigration from the Republic of Poland to Canada Between the Wars HISTORY, HISTORIANS, AND OTHERS 5. Dmytro Doroshenko and Canada 6. General Histories of Ukraine Published in English During the Second World War: Canada, the United States, and Britain 7. George W. Simpson, the Ukrainian Canadians, and the 'Pre-history' of Slavic Studies in Canada 8. The Post-Secondary Teaching of the 'History of Ukraine' in Canada: An Historical Profile 9. Ukrainian Scholarship in the West During the 'Long Cold War' 10. Lubomyr Wynar and the Ukrainian Historical Association in the United States and Canada. 11. In the Shadow of a Political Assassination: Gabrielle Roy's 'Stephen' and the Ukrainian Canadians LIBRARY STUDIES AND REFERENCE WORKS 12. Inveterate Voyager: J.B. Rudnyckyj on Ukrainian Culture, Books, and Libraries in the West during the 'Long Cold War' 13. Scholarship on Mykhailo Hrushevsky during the Early 1980s: Ukrainian Books and Libraries in Canada and the United States 14. Ukrainian Canada in the Encyclopedias, 1897-2010: An Historical Overview CONCLUDING THOUGHTS 15. Ukrainian Canadians and Ukrainian Americans: Some Reflections and Comparisons Appendix: Publishing Histories Index
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".