Recent Changes in the Age Composition of Drivers in 15 Countries
Bibliographic record
Abstract
OBJECTIVE: This study examined the recent changes in the percentage of persons with a driver's license in 15 countries as a function of age. METHOD: The countries included were Canada, Finland, Germany, Great Britain, Israel, Japan, Latvia, The Netherlands, Norway, Poland, South Korea, Spain, Sweden, Switzerland, and the United States. RESULTS: The results indicate 2 patterns of change over time. In one pattern (observed for 8 countries), there was a decrease in the percentage of young people with a driver's license, and an increase in the percentage of older people with a driver's license. In the other pattern (observed for the other 7 countries), there was an increase in the percentage of people with a driver's license in all age categories. A regression analysis was performed on the data for young drivers in the 15 countries to explore the relationship between licensing and a variety of societal parameters. Of particular note was the finding that a higher proportion of Internet users was associated with a lower licensure rate. IMPLICATIONS: The results of the analysis are consistent with the hypothesis that access to virtual contact reduces the need for actual contact among young people.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".