Early driving experience and risk perception in young rural people
Bibliographic record
Abstract
The research initiated from an interest in the area of young drivers and early onset of driving. Many young people who live in a rural or semi rural environment start to drive cars and other vehicles at a very early age, often to help with tasks around a property. Although this research is limited to rural New South Wales, it is anticipated that the findings will have relevance not only to other rural areas within Australia, but also to comparable situations worldwide, or example in US and Canada. In order to gain a deeper insight into these issues, focus groups were held in the three towns. These were with young people, of both sexes and in the age group from 15-21, and also with some adults who were to be supervising drivers for young family members about to start driving on roads as “L” drivers. From the results of the quantitative section of the study, it will be possible to identify factors which may influence or predict driving behaviours and attitudes to driving in young people in a rural or semi rural environment. It will also be possible to identify their personal driving experiences and reasons for these, and their profiles in relation to age, sex, where they grew up, and intentions for continuing with their education. (a) For the covering entry of this conference, please see ITRD abstract no. E217612.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".