Bearing War and Building Peace? Ukrainian Civilians' Everyday Peace Against the Language Severance Amidst War
Bibliographic record
Abstract
Ukraine is a war-torn society polarised by language, among other things. Examining Ukrainian civilians through the scope of everyday peace, a post-liberal local agency is given to people whose identity is threatened. This thesis researches everyday peace across the Ukrainian language divide during war to determine how the war impacts everyday actions, explanations, and the applicability of the theory on interstate wars. The research question is: How do Ukrainian civilians navigate polarised language through everyday peace during war? After conducting eight semi-structured in-depth interviews, they were transcribed and coded according to everyday peace theory. Further analysed by narrative method, recognising burdened, scripted, and transformative narrative agency as focus points for explanations. Abductive reasoning gave results of moderate everyday peace, heavily dependent on contexts of war, trauma brought on by it, societal discourse and Ukrainian determination to transform for peace. This research contributes to the testing of everyday peace across new contexts as well as giving Ukrainians agency and a voice in an oppressive war within an academic field where perspectives of peace are particularly rare.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".