The section "Historical Information Science" at the "Lomonosov" conference: observations over a quarter of a century
Bibliographic record
Abstract
The article analyzes the evolution of the topics presented at the "Historical Information Science" section of the international conference for young scientists "Lomonosov" from 2001 to 2025. The study covers 312 reports presented over 25 years and examines the growth dynamics of the section, thematic and methodological shifts, the institutional structure of participants, technological transformations in research practices, and changes in scientific priorities in the field of historical information science. Special attention is paid to the transition from classical quantitative methods and databases to technologies for three-dimensional modeling, big data analysis, and artificial intelligence, reflecting the digitization of historical knowledge. Using the topics of the reports as an example, the transformation of research themes is traced: from the analysis of land surveyors' books and statistics on workers' complaints to virtual reconstruction of architectural objects and the use of chatbots for working with archives. The work employs a combination of quantitative and qualitative analysis methods: statistical processing of report metadata, analysis of abstracts, visualization of thematic dynamics, as well as the historical and scientific reconstruction of the development of historical information science in the works of young scientists. The analysis of one of the leading Russian platforms in historical information science has revealed not only general trends in the development of historical knowledge in the context of the "digital turn" but also the specifics of the Russian research tradition. The article establishes that the leading role in the development of the field is played by Lomonosov Moscow State University, which acts as a methodological center around which regional scientific schools are formed. An evolution has been identified from instrumental use of computer methods to the establishment of an independent research paradigm. Historical information science in the papers of young scientists is presented as a cumulative development of technologies and an increase in interdisciplinarity. It should also be noted the growing role of young researchers mastering AI and machine learning technologies. The results obtained may have practical significance for the formation of educational programs in the field of historical information science. The study shows that the "Historical Information Science" section serves as a crystallization point for the issues of the scientific community of young scientists, adapting global methodologies to address the challenges of domestic historical research.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".