Bibliographic record
Abstract
Although I did not realize it, I launched this project already in 1996, when I first travelled to Nizhnii Novgorod under the auspices of an undergraduate study abroad program.I arrived knowing next to nothing about Russia, not even the language, and I lacked experience in overseas travel.Greeted hospitably, I found what others might have viewed as a dull provincial city to be large and interesting -not only because it was Russian, but also because I grew up in a rural community.Even my undergraduate institution was surrounded by farmers' fields.Any city anywhere would have offered adventure; being a Russian city, Nizhnii Novgorod added the delightful challenge of navigating a foreign language and culture, both of which I soon came to love.I owe my ability to conduct serious research on Nizhnii Novgorod to a host of individuals whom I met on that very first trip.Staff at the International Office of Nizhnii Novgorod State University offered both friendship and assistance, helping me obtain travel visas, access to the archives, and strong scholarly connections.Olga Artamonova offered friendship, a place to stay, and expert language consultation, as well as access to books at the Pedagogical University.The Zhuravin and Elsukov families likewise offered literature and advice, together with all the comforts of home -kindness, superb food, and a warm and clean bed.The specialists upon whom I relied in the course of my research offered a highly welcome mix of warmth and professionalism.The archivists at GOPANO and TsANO -the former party and state archives of Nizhnii Novgorod, respectively -taught me how to navigate the Russian archives, and in 2001 they offered generous assistance as I gathered my materials.Staff at the provincial library dug old newspapers out of the basement of the church in which the library was then located, a dusty and unpleasant
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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 | 0.005 | 0.020 |
| 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 | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.279 | 0.186 |
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".