From Childhood in a Ruined German City to Research on Crime and Violence
Bibliographic record
Abstract
Friedrich Lösel was born in Germany in 1945. He is Emeritus Professor at Cambridge University (UK), as well as Erlangen University and Berlin Psychological University in Germany. He received the Stockholm Prize in Criminology, the Sellin-Glueck Award from the American Society of Criminology, and the Joan McCord Award from the Academy of Experimental Criminology. He created the Erlangen–Nuremberg Development and Prevention Study (ENDPS), which combined a prospective longitudinal and experimental design and investigated more than 600 children and their families from kindergarten to adolescence. The ENDPS showed that accumulated individual and social risk factors at preschool age predicted behavior problems in youth, but there was also developmental flexibility. The prevention part of the ENDPS implemented a universal training of child social skills, a parent training on positive parenting, and a combination of both. There were substantial short-term effects and promising outcomes after 10 years. The ENDPS team trained about 2,000 facilitators for a nationwide dissemination of the program. He also carried out an important longitudinal study on school bullying showing that intensive bullying perpetration was not only a school phenomenon but correlated with violence in other contexts and with criminal behavior in adulthood.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".