Bibliographic record
Abstract
This article provides an analysis of how tensions in local communities in Ukraine evolved from 2018 to 2023 and the forces driving them. Using ethnographic data, the authors profile four facilitated dialogues to illustrate the dynamics that sparked and quelled discord and the important role religious actors play in facilitated dialogues. They illustrate why and how the arc of conflict has rapidly changed under the draconian conditions of war. Drawing on Chantal Mouffe’s work on pluralism, they argue that social tensions in Ukraine prior to the full-scale invasion in 2022 were primarily of an agonistic nature. They involved disputes between adversaries, rather than antagonistic conflicts between groups. The most frequent adversary was the state. The disputes reflected grievances stemming from economic struggle rather than hostility among groups. Religious actors, regardless of their confession, can potentially play a positive role in the stimulation of non-violent collective action to transform tensions and build social solidarity and social cohesion. However, religious actors are equally capable of exacerbating the tensions that prolong conflict. The authors assess what is at stake in the success or failure of these initiatives to create a culture of dialogue to mediate social tensions in the context of war.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".