“How to March at the Computer”: The Role of Digitalization in the Activities of the Regional Patriotic Organizations of Siberian Federal District
Bibliographic record
Abstract
In Russia, the government’s demand for the patriotic education of young people is constantly growing. However, the content of the programs, their implementation strategies and the prospects for introducing digital technologies into the activities of patriotic youth NGOs remain vague. Based on the analysis of online resources, including the organizations’ social media accounts, the authors conclude that informative content prevails. In addition, they distinguish 4 clusters of non-commercial organizations: Yunarmiyan (Young Army Cadets National Movement), military-athletic, historical and civic, with 60 000 members in total. With the help of TargetHunter parser, the study analyzes social media posts, paying attention to their content and format, the number of posts, likes, comments, viewers and followers. The authors conclude that the level of online involvement has risen as the amount of news traditionally increases in the first quarter of each year, as well as due to the adaptation to the conditions set by the pandemic. The digitalization of patriotic education is complicated and diverse because of the specifics of patriotic organizations, as patriotic content is second to entertainment and educational content on the web.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".