Bibliographic record
Abstract
At the present stage of international information and communication network the Internet is actively used for hosting extremist material. The problem is global in nature and highly relevant for the Russian Federation as one of the main players on the global political process. Using the global network Internet and computer communication, the ideologists of extremist movements and groups are actively working on citizens’ consciousness and first of on youth. As a result, in recent years there has been an aggravation of the problem of extremism, which currently can be seen as a problem of national importance and a threat to national security of Russia.In 2013-2014 years a number of heads of law enforcement bodies, executive and legislative authorities of the Russian Federation, including the President Putin noted the necessity of toughening of the current legislation and measures of a repressive nature for persons committing offences using the Internet, as well as creation of well-developed, adequate system of prevention of this type offences.National security in the sphere of information technologies, improvement of the legal framework and the development of new technical means to combat the spread of extremist ideas in the information space of Russia are the one of the priority tasks, both for public authorities and law enforcement agencies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".