The law isn't everything: The impact of legal and non-legal sanctions on motorists' drink driving behaviors
Bibliographic record
Abstract
INTRODUCTION: The effectiveness of drink driving countermeasures (such as sanctions) to deter motorists from driving over the legal limit is extremely important when considering the impact the offending behavior has on the community. However, questions remain regarding the extent that both legal and non-legal factors influence drink driving behaviors. This is of particular concern given that both factors are widely used as either sanctioning outcomes or in media campaigns designed to deter drivers (e.g., highlighting the physical risk of crashing). METHOD: This paper reports on an examination of 1,253 Queensland motorists' perceptions of legal and non-legal drink driving sanctions and the corresponding deterrent impact of such perceptions on self-reported offending behavior. Participants volunteered to complete either an online or paper version of the questionnaire. RESULTS: Encouragingly, quantitative analysis of the data revealed that participants' perceptions of both legal sanctions (e.g., certainty, severity and swiftness) as well as non-legal sanctions (e.g., fear of social, internal or physical harm) were relatively high, with perceptual certainty being the highest. Despite this, a key theme to emerge from the study was that approximately 25% of the sample admitted to drink driving at some point in time. Multivariate analyses revealed six significant predictors of drink driving, being: males, younger drivers, lower perceptions of the severity of sanctions, and less concern about the social, internal, and physical harms associated with the offense. However, a closer examination of the data revealed that the combined deterrence model was not very accurate at predicting drink driving behaviors (e.g., 21% of variance). PRACTICAL APPLICATIONS: A range of non-legal deterrent factors have the potential to reduce the prevalence of drink driving although further research is required to determine how much exposure is required to produce a strong effect.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".