Bibliographic record
Abstract
A cknowl e dgm e ntsThis book represents a highly interdisciplinary proj ect, but this was not my original intention.Rather, the richness of my sources-and, eventually, the demands of my argument-forced me to become a dilletante in several previously unfamiliar fields.For this reason, the advice, feedback, and support that I received from several individuals was all the more indispensable.Above all, my sincere gratitude goes out to my mentors, Heather Coleman, Dan Healey, and Polly Jones.Their guidance with the intellectual shape of the book, as well as their assistance with practicalities, cannot be understated.Their generous commentary, steady encouragement, and unfaltering willingness to help undergirds this entire manuscript.I am also thankful to Steve Smith and Jan Plamper, as well as to the participants of the University of Alberta East Eu ro pe anists' Circle and the Mid-Western Rus sian History Workshop, for their incisive reading and invaluable advice on earlier drafts.Importantly, I am grateful for feedback from two anonymous readers, as well as the reviewers of Slavic Review, in which an earlier version of the second chapter appeared, under the title "Stalinist Futurity and Historicist Architecture" (fall 2020 issue).I am also thankful to colleagues-
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.254 | 0.171 |
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".