The tooth fairy, Santa Claus, and the hard core drinking driver
Bibliographic record
Abstract
In recent years, the alcohol industry1–5 and certain traffic safety organizations6–9 have tried to draw a sharp distinction between so-called “social drinkers” and “hard core drinking drivers”. We are led to believe that great progress has been made among social drinkers over the last 20 years, and that this extremely large group invariably drinks “moderately” or “responsibly”. In contrast, little progress has been made among the tiny fraction of drivers who make up the hard core drinking driver population. We are urged to “crack down” on this dangerous minority with tougher penalties, particularly for repeat offenders.9 Broader enforcement measures and lower criminal blood alcohol concentration (BAC) limits are to be avoided, as they would unnecessarily alienate social drinkers. The hard core and social drinker rhetoric creates a convenient scapegoat for Canada's and, no doubt, other countries' impaired driving problems. By blaming hard core drinking drivers, proponents of these stereotypes allow mainstream “social drinkers” to separate themselves from the impaired driving issue, without ever having to critically assess their own drinking and driving habits. For example, in a 1999 article, the President of the Brewers Association of Canada recommended that legislative measures be focused on the “small minority of drivers” who are the “real cause of the problem”.1 He suggested that “great strides” have been made in reducing impaired driving among the general population, and that “hard core” offenders are the last remaining bulwark of irresponsible drinking and driving habits. The President describes this small minority as “repeat offenders, [who] often continue to drive with a suspended license, and remain indifferent to societal pressures to reform”. This mischaracterization of the impaired driving problem limits the reform agenda. For instance, Canada's federal government recently ignored calls for sweeping reforms to the impaired driving law in its …
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".