To the anniversary of Professor, Doctor of Geography Irina Rodionova - a woman who works to make the world a better place
Bibliographic record
Abstract
Doctor of Geography, Professor Irina Alexandrovna Rodionova - recognized specialist in the field of industrial geography. For more than a quarter of a century she gave to work at the Lomonosov Moscow State University and about the same number - work at the Peoples Friendship University of Russia. Her life is full of events and meetings with interesting people. There were also difficulties. It was especially hard for scientific and pedagogical workers in the 1990s, when their work was poorly funded. Specialists did not remain in science, but Irina Alexandrova did not leave it. In the 1990-2000s, many students knew I.A. Rodionova from textbooks. Using them, they entered universities in geographical and economic specialties. 2021 was also a difficult year, when distance learning was introduced. Conferences were also held remotely. Irina Alexandrovna coped with these problems, successfully working both in the teaching and in the scientific field. She generously shares her knowledge and experience with colleagues and students and is the developer of a number of disciplines taught at universities. Under her leadership, 10 dissertations were successfully defended. I.A. Rodionova is the author of more than 400 publications, many of which she published in co-authorship with colleagues and students. She took part and created many textbooks for universities on the geography of industry, economic geography, and is one of the authors of the atlas for the school textbook on geography of grades 10-11. This article - a small fraction of gratitude from colleagues and students in honor of the anniversary of I.A. Rodionova.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.097 | 0.112 |
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".