Wartime Civilian Mobilization: Demographic Profile, Motivations, and Pathways to Volunteer Engagement Amidst the Donbas War in Ukraine
Bibliographic record
Abstract
Abstract This article examines civilian mobilization amidst the Donbas war, Ukraine. It focuses on ordinary residents of the frontline regions who voluntarily got together to address the humanitarian and military consequences of war in the environment of lacking state support. It explores the micro-level dynamics of mobilization, particularly the demographic profile of civilian volunteers, their motivations to join, and pathways to engagement. In so doing, it provides an account of how ordinary residents of seemingly passive regions – Southern and Eastern Ukraine – become active in times of crisis. Contrary to the mainstream accounts that credit civilian mobilization to the rise of patriotism in wartime, it demonstrates that local security concerns and affective reactions to the heightened precarity of others are crucial factors that propel collective action at the rear. In the case of Ukraine, the efficiency of wartime mobilization was increased through the structures that emerged during the proceeding Maidan protests, as well as preexisting private and entrepreneurial networks. By employing ethnographic tools of inquiry, the article interrogates the mobilizing potential of seemingly latent communities in times of crisis and contributes to the literature on wartime collective action at the rear.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".