Open Access: Information about the Great Patriotic War in the Space of Telegram Channels
Bibliographic record
Abstract
The virtual environment has become the main source of information for the modern average person, student and researcher. In order to obtain data on the Great Patriotic War, one turns to the anniversary year of the Victory much more often. On the Internet, information about the events of the war is disseminated not only through the official websites of scientific institutions, museums and historical centres, but also through pages and channels in social networks. Created for everyday communication, they provide an opportunity for amateurs to broadcast their own opinions to an unlimited audience. The aim of the publication is to show how information about the events of the Great Patriotic War “lives” in the space of messengers. Based on statistical data on the use of platforms, the author of the article examines attempts to represent the topic on Telegram, the second most popular platform among the Russian audience. Publications were analysed both in official channels and in amateur channels. Certain differences in the presentation of data, both in the forms of publications and in the quality of information are revealed. The channels specific to this platform - chatbots (automatic interlocutors with a given algorithm) dedicated to the Great Patriotic War - are analysed separately. Based on the analysis of the array of publications for the first quarter of 2025, the authors show how the memory of the war in the virtual space of social networks is represented in the anniversary year.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".