Violence in Yemen: Thinking About Violence in Fragile States Beyond the Confines of Conflict and Terrorism
Bibliographic record
Abstract
This article examines the different forms of criminal violence that affect fragile states, with special reference to Yemen. The article is particularly interested in analysing the relationship between violent offending with no clear political motive, underdevelopment and conflict. It does so by conducting an in-depth evaluation of conflict and crime in Yemen, using publically accessible data to suggest new ways of understanding violent criminal behaviour in Yemen and elsewhere. This article is written in response to a prioritisation of political violence, insurgency and terrorism in international development and stabilisation strategies, which has emerged alongside the broad securitisation of international aid. Common forms of criminal violence have been overlooked in a number of fragile contexts, as they have been in Yemen. In light of rising levels of insecurity, resulting from poor relationships between the state and its citizens, there is a need to re-evaluate this unstated omission if the new Yemeni Government is to gain increased legitimacy by being seen to prioritise the protection of its citizens.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".