Wartime Civilian Mobilization: Demographic Profile, Motivations, and Pathways to Volunteer Engagement Amidst the Donbas War in Ukraine
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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