Bibliographic record
Abstract
grown and changed because of the kindness and wisdom of many people who helped me work my way through the musical, historical, political, cultural, and other labyrinths that made the Soviet Union what it was during Gregory Haimovsky's diffi cult life in the USSR.These people not only enabled me to understand the many contexts I discuss in this book, they continuously supported and encouraged me.First among them are the late Katerina Haimovsky, the late Janna Kavounovsky, the late Tamara Dalmat, Vladimir Nestyev, Mark Baranov, Marina Nestyeva, Luda Medova, Oleg Milman, Zoya Porotskaya, and the late Gennady Shokhman.Special gratitude goes to Kira Haimovsky who, for more than two decades, opened her home to me on countless occasions.Without her blessing, guidance, and consideration, this book would not have been possible.Were it not for Galina Lebovskaya, who worked to retype the Cyrillic Russian manuscripts and documents, my efforts would have been seriously compromised.Also, Ella Wilcox read early drafts of this book.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.295 | 0.200 |
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".