Bibliographic record
Abstract
It is hard for me to remember a time when I was not interested in Russia.It all started in a library.One book on the Russian Revolution led to another, which led to investigations of Soviet and Russian culture, international relations, and Cold War antipathies.Most of my high school history projects dealt in some way with the USSR and North American perceptions of it, facilitated by the mass of information made public in those heady days of Mikhail Gorbachev.By the time that I got to university, the USSR was no more, and my goal was to get over there so that I could balance my bookish learning with real experiences.Immediately following the completion of a master's degree in Central/ Eastern European and Russian-Area Studies, I found a job in Kyiv, Ukraine.What was meant to be a summer internship turned into a five-year hiatus in Ukraine, where I lived in Kyiv and Kharkiv, travelled throughout the region, met and married my husband, and spent the best part of a year with my oldest son.Although as foreigners we lived in privileged conditions, the economic and social aspects of life in the former USSR had an impact on our everyday lives.The people whom I met there were struggling to overcome seventy-five years of poor policy with limited success.I have nothing but admiration for what they have achieved in the face of enormous obstacles.If on one level I can understand the motivation of some Canadians to join the Canadian-Soviet Friendship Society (CSFS), a group that allowed them to visit the USSR, on another level I cannot ignore the fact that they were co-opted into legitimizing a message that was part of a larger Soviet Potemkin village.Negotiating these two levels of understanding, and balancing an appreciation for idealism with a realistic and healthy dose of scepticism, have comprised ongoing conversations throughout this project.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.562 | 0.388 |
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".