Bibliographic record
Abstract
Alcohol: the ubiquitous risk factorThis issue includes four papers that draw attention to one of the most important causes of injury in all age groups and in every part of the world-alcohol.In particular, the papers address impaired drivers.The first is an original article pointing to the benefits of laws that penalize drivers whose blood alcohol content (BAC) exceeds 0.08% by weight (p 109).In view of the growing evidence to which this paper adds substantially, it is diYcult to understand why so many constituencies tolerate higher limits.It is even more diYcult to fathom why, whatever the limits, more impaired drivers are not apprehended or, when they are, why they are not punished more severely.These issues are addressed in the second paper-one that we hope will be the first of many written by from a lawyer's perspective (p 96).The senior author, Robert Solomon, is the legal advisor to the Canadian branch of Mothers Against Drunk Driving (MADD).Although the statistics in this paper only reflect the experience in one province, my guess is that the issues these data point to are commonplace, not only elsewhere in Canada but also world wide.In spite of limitations in the data system itself, the findings clearly point to serious flaws in how both the police and the legal system address impaired driving.Set aside the legal subtleties and look at the larger picture the findings present.As someone who has been spared personal experience with drunk driving, I find the results extremely disturbing.Imagine, then, how the families of victims must feel about this "system" that is supposed to deter.Because most injury prevention initiatives depend on lay persons for their success, I asked the vice president of MADD in the US, Wendy Hamilton, to present it as a Featured Program (p 90).Anyone familiar with this amazing program is bound to be impressed with its many accomplishments.Sadly, its origins and continued strength derive almost entirely from needless personal tragedies.There are many such programs in other countries and each deserves similar recognition.Finally, because impaired driving is dealt with so diVerently from one country to another, I asked Kathryn Stewart of the Pacific Institute for Research and Evaluation to write a guest editorial on this issue (p 80).She graciously agreed.Her superb contribution draws attention to how international the eVort has been to conquer this problem.Not surprisingly, perhaps, the country with the lowest tolerated level is Sweden, 0.02%.Stewart's editorial also
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".