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
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".