Bolivar can’t carry double? The impact of the Israel-Hamas war on media coverage of the Russia-Ukraine war
Bibliographic record
Abstract
The article describes how the first six months of the armed conflict in and around the Gaza Strip impacted political and media discourses about Russia’s full-scale invasion of Ukraine, which started on 24 February 2023, in five countries: the two belligerents, the United States, the United Kingdom, and France. It shows that after 7 October 2023, the attention of Western leaders and media was distracted from the situation in Ukraine and redirected to the Gaza Strip. The sampled social media, VKontakte and Telegram, reacted to the military operations in the Gaza Strip mostly in unison with legacy media. Before and after the start of the Israel-Hamas war, sources of political and media discourses formed national clusters. The corpora containing more than 218 million words in four languages, Ukrainian, Russian, English, and French, informed the analysis. In addition to social media, the corpora include speeches from political leaders and news items about Russia’s invasion of Ukraine, run by seventeen legacy media outlets (newspapers, online news portals, and T.V. channels).
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".