Why don't northern American solutions to drinking and driving work in southern America?
Bibliographic record
Abstract
While individual studies from several South American countries have shown driving while intoxicated to be a problem, there are no objective systematically collected alcohol-associated driving data obtained in most South American countries. This limits their ability to implement and enforce targeted prevention strategies, evaluate whether proven prevention efforts from North America (particularly the United States and Canada) can be transferred to the South, and to sustain momentum for the improvement of road safety by demonstrating that previously implemented legal and policy changes are effective. The aim of this paper is to discuss the abysmal differences that exist between northern and southern American countries regarding the current status of driving while intoxicated prevention strategies-their implementation, impacts and effects-using Brazil as a case example. We propose a three-pronged approach to close this northern-southern American gap in driving while intoxicated prevention and intervention: (a) systematic collection on road traffic crash/injury/death as well as risk factor data, (b) passage of laws without loopholes requiring compliance with blood alcohol concentration testing and (c) provision of appropriate training and equipment to the police in concomitance with vigilant enforcement. Resources and energies must be put towards data collection, implementation of prevention strategies and enforcement in order to decrease the unacceptably high rates of these preventable driving while intoxicated deaths.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".