Dataset of posts from foreign news agency sites by tag "Russia" in 2022-2024
Bibliographic record
Abstract
The dataset consisted in .XLSX tables divided by countries, and includes columns: data, source, title, fulltext and link (for verifying). The data was collected by students of Department of Politology, History and Regional Studies of History Faculty, Irkutsk State University (Russia). Advisor investigator is Prof. Arseniy N. Fartyshev, Candidate of Sci. (in Geography). Country Number Date coverage Advisor Sources Turkey 802 2007-2024 Bogdanova Milena Evgenevna YeniSafak, AA, Sabah, Haber7, Dailysabah, Cumhuriyet, Aksam Israel 310 2021-2024 Bukina Maria Vasilievna Yated Croatia 488 2021-2024 Vdovkin Aleksei Igorevich Net.hr, tportal GB 526 2022-2024 Dashinimaeva Selmeg Chingisovna The Economist, The Guardian, BBC Syria 1595 2013-2024 Demchenkov Mihail Andreevich Sana Japan 1426 2024 Korytov Andrei japannews.yomiuri Serbia 9733 2021-2024 Litovchenko Ivan Aleskandrovich Telegraf.rs USA 823 2023-2024 Mineev Mihail Aleskandrovich New York Times Germany 2322 2023 Monastyrskiy Daniil Sergeevich N-TV Armenia 1119 2021-2024 Perendya Daria Aleskeevna Aravot Sweizerland 2795 2021-2024 Ryzhov Andrei Dmitrievich Watson.ch Greece 2863 2012-2024 Sadykova Maria Alekseevna Enikos South Korea 389 2021-2024 Sannikova Elizaveta Sergeevna choson China 873 2023-2022 Fomina Sofia Ivanovna Xinhua Finland 1233 2021-2024 Tsyrendorzhieva Valeria Timurovna Yle Italy 869 2022-2024 Segreeva Elizaveta Antonovna rainews.it Canada 2146 2024 Fedorova Anastasia Andreevna nationalpost Venezuela 407 2022-2024 Topuchkanov Dmitriy Sergeevich El Aragueno, Lapatilla, Noticia al Dia TOTAL 30719
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.041 | 0.069 |
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".