Analysis of the Effects of Large-Scale Disasters on the Behavior of Non-State ViolentActors in Countries Already Under Duress
Bibliographic record
Abstract
Over the course of human civilization, humanity has been exposed to major disruptions of normalcy due to the onset of naturally occurring large-scale disasters. In the midst of disaster, some societies have also dealt with intrastate conflicts that strain the already depleted resources necessary for the survival and support of established institutions and governance. At the forefront of my research is the ongoing COVID-19 pandemic which established itself in the fourth quarter of 2019 and has since then taken hundreds of thousands of lives and deteriorated several economies around the world. For this paper, I will be analyzing the effects that disasters have with respect to the behavior of non-state violent actors (NSVAs) and subsequent influence on conflict continuation, escalation, and de-escalation, as well as the NSVAs ability to legitimize themselves in the view of the state and international community. I will be drawing on existing literature, including the Ripe Moment Theory presented by Joakim Kreutz, to help explain behavior and conflict before, during, and after a disaster; will be utilizing the ACLED ConflictDatabase to track conflict intensity both before and during the pandemic; and will be providing case studies from multiple regions around the world to provide different contexts for distinctNSVAs. At the end of this paper, I come to the conclusion that the behavior of the NSVA and conflict continuation, escalation, and/or de-escalation, in the midst of a disaster, is both moderately dependent of the condition of the regime type, as well as the overall ambitions of the actors in question, whether that be political motivations or motivations of other interests.Additionally, I argue that states and NSVAs must come to a mutual agreement in the establishment of relief services, and that states should attempt to negotiate with the NSVA in the facilitation of the appropriate resources and guidance.
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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 | 0.001 | 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 | 0.000 | 0.001 |
| 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".