Bibliographic record
Abstract
This is the second of two volumes that showcase young scientists who are continuing the outstanding tradition of Russian mathematics in their home country. There remain numerous strong research groups, particularly in Moscow and St. Petersburg, despite the familiar difficulties: academic salaries in Russia remain low, many leading figures have departed and there are plentiful opportunities for employment in university positions abroad or in sectors in Russia that offer a living wage. It is hoped that the articles in this book give a picture of the interests and achievements of mathematicians that participate in some of the active seminars in the country. Seven have something of the character of a survey, but also contain many original results and give extensive bibliographies; the eighth is a revised and expanded version of a 2002 research article. The first of the two volumes (LMS Lecture Notes 338) was entitled Surveys in Geomety and Number Theory ; this one is mainly on combinatorial and algebraic geometry and topology. Both volumes contain papers based on courses of lectures given at British universities by the authors under the ‘Young Russian Mathematicians’ scheme, which the London Mathematical Society set up to help such mathematicians visit the UK and to provide them with financial support. In the nineties sheer subsistence was difficult for Russian academics. Over the last five years things have improved, and the salaries of university employees, though not generous, are closer to sufficing for the necessities of life.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.390 | 0.219 |
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".