Bibliographic record
Abstract
Oksana Dudko shares her experience of teaching Ukrainian history in Canadian universities during Russia's aggression. She notes the fundamental difference between students in Ukraine and students in Canada, many of whom take a class in Ukrainian history having little prior knowledge about the country. So, whereas in Ukraine, critically thinking university lecturers concentrate on deconstructing the simplified national historical narrative that has been interiorized by students in secondary school, in Canadian classrooms professors have to offer a coherent historical narrative that includes advanced methodological considerations. Another challenge is the dearth of reading materials. The available collections of primary sources translated into English are Russo-centric both in terms of document selection and the translation of key terms and concepts. Dudko stresses the importance of contemporary artistic sources, including visual ones, for expanding students' understanding of Ukraine. She also emphasizes the advantage of Ukraine's stateless status throughout much of its history and its fluctuating territorial boundaries for teaching modern postnational and post-state history, unconstrained by the traditional narrative of the nation-state.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.032 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".