Bibliographic record
Abstract
This unusual book is a biography of Baron Roger Budberg, an ethnic German from what is now Latvia, and a history of what is now the Chinese city of Harbin, where he lived much of his life. Harbin: A Cross-Cultural Biography is constructed with chapters telling Budberg’s life story alternating with others on the history of life in Harbin, from its foundation on the new Russian railway line in 1898 through to the 1950s, when its remaining Russian population departed. The book is obviously the result of many years of research in various languages (Chinese, Russian, and German being only the main ones), and like many microhistorians, Gamsa is a lover of detail. His central themes are the attitudes of Chinese and Russian residents of Harbin to each other and the variety and instability of individual national identities, but these themes are often subordinate to Gamsa’s pursuit of a wide cast of characters in Russia, France, Ukraine, and as far afield as Israel and Brazil. Much of the interest of the book depends on one’s interest in those particular stories and angles. Personally, I was most interested in the events of the 1900 Boxer Uprising in Harbin and the attitudes of Russians in northeast China, but other readers may be just as interested in the history Baltic Germans in the Russian Empire or the pidgin of early twentieth-century Harbin.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.088 | 0.034 |
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".