Com conduïm després d’un programa formatiu de seguretat viària?
Bibliographic record
Abstract
Els programes formatius de seguretat viària són una mesura que s’aplica des de l’execució penal a la comunitat en delictes de trànsit i tenen com a finalitat reduir la reincidència dels conductors de major risc. L’objectiu principal del treball és identificar les característiques comunes dels infractors de trànsit que fan aquest tipus d’intervenció, conèixer els factors de risc associats a aquests infractors i en quina mesura l’estat psicològic és un factor de risc en l’estil de conducció. També es volia determinar si hi havia diferències entre les entitats que impartien la formació i avaluar l’efectivitat d’aquests programes en l’estil de conducció dels participants en finalitzar el curs. En l’estudi, hi van participar 278 voluntaris del total de 354 infractors de trànsit que van fer el programa formatiu entre l’1 d’abril de 2009 i el 13 de febrer de 2010.\n\nHow do we drive after a driving educational program? \n\nThe study analyzes the results of educational programs that are applied as a community sanction for those convicted for driving offenses, in majority drunk driving, in Catalonia. Between April 1, 2009 and February 13, 2010 a total of 278 offenders participated in these mandatory educational programs.\n\n¿Cómo conducimos después de un programa formativo de educación vial? \n\nEl estudio analiza los resultados de los programas formativos que se aplican como medida penal alternativa a la prisión a los condenados por delitos de tráfico en Cataluña. En total han participado 278 infractores que realizaron el programa formativo entre el 1 de abril de 2009 y el 13 de febrero de 2010.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".