Falsehood in Wartime: Hiding Truths and Spreading Untruths in Russia During the First Year of Putin’s War in Ukraine’
Bibliographic record
Abstract
The old adage that truth is the first casualty of war held fast during the build-up to, and the conduct of, the ‘special military operation’ (SVO) launched by Vladimir Putin against Ukraine on 24 February 2022 is no exception to the rule. The Russian state’s methods for sustaining its changing official narrative about the SVO are detailed here, as are the ways and means the dissident portion of Russia’s citizenry made their objections to the war in Ukraine heard during its first year. The Kremlin’s already firm grip in the distribution of information was tightened. Russia’s wordsmiths were silenced, jailed or fled the country. School curricula were re-organized so that they became incubators of young Russian patriots. The paper concludes with conjecture on why the state’s message found such a welcoming audience among Russians, at least during the first year of war. Les réponses du Kremlin aux voix pacifistes en Russie : annoncer la couleur pendant la première année de la guerre L’« opération militaire spéciale » (SVO), déclenchée par Vladimir Poutine contre l’Ukraine le 24 février 2022, a surpris la majorité des Russes. Il a fallu les persuader de sa justesse, par tous les moyens possibles. Cet article détaille les méthodes employées par l’État russe pour appuyer son discours officiel changeant au sujet de la SVO ainsi que les moyens trouvés par les groupes dissidents de la société civile pour faire entendre leur voix durant la première année de la guerre. Le Kremlin a affermi sa poigne déjà puissante sur la distribution des informations tandis que les opposants vocaux ont été réduits au silence, emprisonnés ou forcés de fuir le pays. Les programmes des écoles ont été modifiés afin qu’elles servent d’incubateurs de jeunes patriotes russes. L’article se conclut par une conjecture sur les raisons qui ont poussé le public russe à accepter le message étatique si chaleureusement, du moins pendant la première année de la guerre.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".