Bibliographic record
Abstract
This project has enjoyed and endured a long journey over the past decade.At its heart are a number of complex problems that have often stumped and consumed me, not least Russia's 2014 annexation of Crimea, which threw this project and its assertions into turmoil.Putting all this together has been a challenge that has been steered and supported by many along the way.I am especially grateful to my PhD supervisor, Denisa Kostovicova, who gave the project the space and intellectual probing that it needed from the outset, as well as my PhD advisor, John Breuilly, who provided the scholarly backbone of nationalism studies, out of which this project grew.I am also grateful to Richard Ratzlaff, Edwin Janzen, and McGill-Queen's University Press for their support in publishing this book, and the two anonymous reviewers who invested a great deal in providing constructive suggestions that helped immensely to transform this from manuscript to book.I am both grateful and indebted to the research participants of this project in Simferopol and Chis ¸ina ˘u, who gave their time and insights, and to my hosts in both cities, who shared their homes.I hope this work pays at least some tribute to their immense contribution and daily struggles, especially in Crimea.I am also grateful for the assistance of Julija Ogarkova, Anna Boyce, Alexandra Sta ˘nescu, and Andra Ora ˘s ¸anu in transcribing the interviews.I am also extremely
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.337 | 0.246 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".