The narratives of war (NoW) corpus of written testimonies of the Russia-Ukraine war
Bibliographic record
Abstract
Documentation and analysis of psychological states experienced by witnesses and survivors of catastrophic events is a critical concern of psychological research. This paper introduces the new corpus of written testimonies collected from nearly 1500 Ukrainian civilians from May 2022-January 2024, during Russia's invasion of Ukraine. The texts are available in the original Ukrainian and the English translation. The Narratives of War (NoW) corpus additionally contains demographic and geographic data on respondents, as well as their scores in tests of PTSD symptoms and moral injury. The paper provides a detailed introduction into the method of data collection and corpus structure. It also reports a quantitative frequency-based "keyness" analysis that identifies words particularly representative of the NoW corpus, as compared to the reference corpus of Ukrainian texts that predates the war with Russia. These key words shed light on the psychological state of witnesses of war. With its materials collected during the ongoing war, the corpus contributes to the body of knowledge for studies of the psychological impact of war and trauma on civilian populations.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".