Road users' socio-economic status and road safety in Denmark
Bibliographic record
Abstract
The goal of this study is to investigate if there specific socio-economic groups in Denmark which are at increased risk to be involved in road accidents. All Danish residents in year 2000 older than 15 years (4,297,373) were considered. Age, gender, income, education, origin and criminal history were chosen to represent the subjects' demographic and socio-economic characteristics, while involvement in road accidents during the study period was used to represent his/her accident risk. The accident risk (in terms of odds ratio, O.R. and number of accidents a person was involved in) was evaluated by logistic and Poisson regression. The highest odds for being involved in road accident were found among individuals with a criminal record: O.R.=3.9 (95% CI 3.6-4.2) for persons who committed only non-traffic law violations and O.R.=13.3 (95% CI 11.7-15.1) for people with both traffic and other laws violations as compared to the non-criminal group. Young age and being a male were also associated with higher odds to be involved in an accident, while the odds decreased with increasing of the education level. The origin of the person was found not to be significant. A decreasing of the average age with increasing of the number of accident a person was involved in was observed. The paper discusses the need of specific road safety interventions targeting high risk groups.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".