The narratives of war (NoW) corpus of written testimonies of the Russia-Ukraine war
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it