Testing and Contrasting Road Safety Education, Deterrence, and Social Capital Theories: A Sociological Approach to the Understanding of Male Drink-Driving in Chile's Metropolitan Region.
Bibliographic record
Abstract
Three theories offer different explanations to the understanding of male drink-driving. In order to test road safety education, deterrence, and social capital theories, logistic regression analysis was applied to predict respondents' statements of having or not having engaged in actual drink-driving (DD). Variable for road safety education theory was whether a driver had graduated from a professional driving school or not. Deterrence theory was operationalized with a variable of whether a driver had been issued a traffic ticket or not. Social capital theory was operationalized with two variables, having children or not and having religion identification or not. Since both variables 'years of formal education' and 'years of driving experience' have been reported to be correlated to alcohol consumption and DD respectively, these were introduced as controls. In order to assess the significance of each variable statistically, Wald tests were applied in seven models. Results indicate on the one hand that road safety education variable is not statistically significant; and on the other, deterrence theory variable and social capital theory variable 'having children' were both statistically significant at the level of .01. Findings are discussed in reference to Chile's context. Data were taken from the "Road Users Attitudes and Behaviors towards Traffic Safety" survey from the National Commission of Road Safety of the Government of Chile (2005). The sample size was reported to be 2,118 (N of male drivers was 396). This survey was representative of Chile's Metropolitan Region road users' population.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".