Children’s and youth samizdat at the beginning of the XXI century (based on the materials of the Siberian-Far Eastern Region)
Bibliographic record
Abstract
Amateur children’s and youth press (samizdat) has historical roots in Russia, including in the Siberian-far Eastern region. At each stage it was characterized by specific features conditioned by socio-cultural factors of the time. The purpose of the article is to analyze current trends in the development of children’s samizdat. The work is based on an extensive source database. The author analyzed data from “The Russian school press Register”, looked through PDF-versions and online school publications on the official pages of almost one hundred educational institutions in the region, as well as individual printed samples of children’s and youth amateur magazines, studied the sites “Dai Zin”, “Children’s samizdat” and a number of blog communities popular with teenagers. The investigation showed that in the first quarter of the new Millennium the unique phenomenon called samizdat is an actively developing part of the information space. There was a quantitative growth of children’s and youth (teenage) press. Its type-specific and content characteristics have become more diverse. Along with subcultural periodicals prepared by young correspondent, home-made book are also published. The rapid development of new information technologies has resulted in the emergence of new forms of self-publishing: online magazines, blogs of young authors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".