Beyond Greed: Why Armed Groups Tax
Bibliographic record
Abstract
Armed groups tax. Journalistic accounts often include a tone of surprise about this fact, while policy reports tend to strike a tone of alarm, highlighting the link between armed group taxation and ongoing conflict. Policymakers often focus on targeting the mechanisms of armed group taxation as part of their conflict strategy, often described as ‘following the money’. We argue that what is instead needed is a deeper understanding of the nuanced realities of armed group taxation, the motivations behind it, and the implications it has for an armed group’s relationship with civilian and diaspora populations, as well as the broader international community. This paper builds on two distinct literatures, on armed groups and on taxation, to provide the first systematic exploration into the motivation of armed group taxation. Based on a review of the diverse practices of how armed groups tax, we highlight that a full account of their motivation needs to go beyond revenue collection, and engage with key themes around legitimacy, population control, institution building, and the performance of public authority. We problematise common approaches towards armed group taxation and state-building, and outline key questions of a new research agenda.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".