Bibliographic record
Abstract
THIS IS NOT IT'В статье рассматриваются стратегии, которыми популярный музыкант и блогер Сергей Шнуров (группировка «Ленинград») пользуется в целях капитализации собственного бренда, для самовыражения внемузыкальными средствами, формирования собственного публичного образа и обратной связи с фан-базой.Будет предложен анализ принципов взаимодействия автора блога с многочисленными подписчиками канала, логики отбора Шнуровым освещаемых событий из частной и публичной жизни, а также языка его коммуникации -как в сугубо лингвистическом, так и визуальном аспектах.Ключевые слова: Сергей Шнуров, Инстаграм, социальные сети, социальная интеракция, язык лингвистической и визуальной коммуникации, теория селебрификации.The article examines various strategies employed by the popular musician and blogger Sergei Shnurov (punk rock band "Leningrad") in order to capitalize his own brand; these strategies include Shnurov's self-expression with non-musical means, forming his own public image and receiving feedback from the fan base.Leving also offers analysis of the principles of interaction between the author and numerous subscribers of Shnurov's blog, scrutinizes the logic of coverage of events from the musician's private and public life, as well as the language of his communication, in both its verbal and visual aspects.
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 | 0.002 | 0.003 |
| 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 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".