Conference report: Weapons in any context bad for people's health, MDs told
Bibliographic record
Abstract
In early May, as a million soldiers massed in another tense standoff on the India–Pakistan border, 100 doctors from around the globe assembled in Montreal to examine the role public health can play in preventing war-related injuries. The 2-day meeting, organized by the Centers for Disease Control and Prevention, International Physicians for the Prevention of Nuclear War and the World Health Organization, preceded the World Conference on Injury Prevention. The discussions, which covered everything from access to small arms to sexual violence, seemed to come at an opportune time. Not only had violent incidents against civilians escalated dramatically in conflict areas like Sierra Leone and Israel, but death and injury rates due to gunfire were also common in industrialized, high-income countries that are supposedly at peace. This means that Canadian physicians can ill afford to ignore the message that weapons in any context are bad for people's health, doctors were told. Wendy Cukier, a professor of justice studies at Toronto's Ryerson University, said Canada ranked fifth in terms of firearm-related deaths among children in a survey involving 26 industrialized countries. Guns are readily available in about 20% of Canadian households, and about 1000 Canadians are killed with firearms every year. Cukier said the number of children under age 15 killed by guns in Alberta each year is as high as the figure for Israel and Northern Ireland combined. “Mortality rates in places that are supposedly at peace are as high as in countries at war,” Cukier told CMAJ. — Susan Pinker, Montreal l
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.014 | 0.019 |
| Insufficient payload (model declined to judge) | 0.038 | 0.017 |
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".