Bibliographic record
Abstract
Together with Canada and Australia, Europe is worldwide the safest area where (fatal) road traffic injuries are concerned. However, the number of adolescents involved in traffic accidents with fatal outcome (1 in 3,700 injury deaths in 15–24 yr. adolescents each year) remains a matter of high concern. Needless to say governments try to reduce mortality using all kinds of preventive measures. The question then arises why so many of these preventive activities targeting adolescents seem to have no effect at all. One of the most important reasons for that is that many of these activities pay no heed to the fact that in adolescence, the brain is undergoing massive transformations that greatly influence the way adolescents deal with information concerning their (health) behaviour. One textbook example of a wrong way to try to influence adolescents is to show them the horrific consequences of traffic accidents - pools of blood, severed limbs, disfigured faces and the like. Instead of being frightened into safe driving, adolescents tend to conclude that these victims, although pitiable, are just losers - they themselves would know how to avoid such situations, of course. There are many examples of this kind of rather predictable effects, but as designers of preventive activities are mostly adults and do not take into account the effects of brain development in adolescents, preventive activities will in many cases continue to be useless or even detrimental. In this presentation, the development of the brain in adolescents and its effect on (healthy) behaviour will be discussed.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".