Bibliographic record
Abstract
Cukier and Sidel provide a much-needed overview of the global problem of gun violence as a threat to public health, including the effects of violence, the sources of firearms (both legal and illegal), the factors shaping demand, and the interventions aimed at reducing the misuse of guns. Just as guns know no borders, gun violence has become a global epidemic, killing hundreds of thousands of people each year and injuring many more. The toll is staggering. Experts estimate that there are 35,000 annual gun-related deaths in Brazil, 10,000 in South Africa, 20,000 in Colombia, and 30,000 in the United States. While guns kill or maim great numbers of people in war zones, two thirds of small arms are in the possession of civilians. Although guns do not in and of themselves cause violence, they increase its lethality and fuel cultures of violence. This book documents the global gun trade, its threat to public health, and efforts to remedy the situation. Virtually every illegal gun begins as a legal gun. With the globalization of trade in licit products has come the globalization of the illegal trade in guns. For example, weapons originating in the United States fuel violence in Canada, Latin America, and as far away as Japan. And unregulated ownership of guns fuels crime. Because weapons tend to flow from unregulated areas to regulated areas, international cooperation is critical, but global efforts have been hampered by major arms producers and gun lobbies such as the National Rifle Association. Since 1998 there has been an emerging global movement to control the illicit trade and misuse in guns, and many countries have moved to strengthen their gun laws in an effort to combat this global epidemic.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.038 | 0.006 |
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".