An agent–vector–host–environment model for controlling small arms and light weapons
Bibliographic record
Abstract
Armed violence is a significant public health problem. It results in fatal and non-fatal injuries and disrupts social and economic processes that are essential to the health of individuals and communities. We argue that an agent-vector-host-environment model can be helpful in understanding and describing the availability and misuse of small arms and light weapons. Moreover, such a model can assist in identifying potential control points and in developing mitigation strategies. These concepts have been developed from analogous vector control programs and are applied to controlling arms to reduce their misuse. So-called 'denormalization' and 'de-legitimization' campaigns that focus on the vector - including the industry producing these commodities - can be based on the experience of public health in controlling tobacco use and exposure. This model can assist health professionals, civil society and governments in developing comprehensive strategies to limit the production, distribution and misuse of small arms and light weapons.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".