Narratives of the Russo-Ukrainian War A Look Within and Without
Bibliographic record
Abstract
This volume presents a collection of selected essays and interviews published by the Forum for Ukrainian Studies, an online analytical publication of the Canadian Institute of Ukrainian Studies (CIUS) at the University of Alberta, in the first two years after Russia's full-scale invasion. The scholars and experts tackle a broad range of questions about identity, culture, propaganda, security, international relations, history, decolonization, and state of the art in Ukrainian studies and Russian studies in Western academia in the context of the Russo-Ukrainian war. The texts are presented chronologically to illustrate the dynamics of the rapidly unfolding events and contextualize the ideas and narratives that were discussed at particular moments. The interviews and essays provide supremely knowledgeable and nuanced perspectives and analyses of the war, from both within Ukraine and abroad. The selected Forum contributors are Margarita Balmaceda, Mykola Bielieskov, Michael Bociurkiw, Mariana Budjeryn, Dovilė Budrytė, Dmytro Bushuyev, Vitaly Chernetsky, Davis Daycock, Marta Dyczok, Olexiy Haran, Khrystyna Holynska, Aliaksei Kazharski, Yuliya Kovaliv, Hiroaki Kuromiya, Serhiy Kvit, Elżbieta Kwiecińska, Frank Ledwidge, Agnieszka Legucka, Tamara Martsenyuk, Jade McGlynn, Rajan Menon, Alexander Motyl, Cynthia Nielsen, Donnacha Ó Beacháin, Mitchell Orenstein, John V. Parachini, Bo Petersson, Serhii Plokhy, Yevhenia Podobna, Oleksii Polegkyi, Maryna Shevtsova, Marci Shore, Nataliya Shpylova-Saeed, Polina Sinovets, Ewa Thompson, Iryna Tsilyk, Peter Vermeersch, Alexander Vindman, Mychailo Wynnyckyj, Andrii Zharikov, and Mariia Zolkina
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".