On the future of the individual longitudinal age‐crime curve
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: This article serves as our memorial for the outstanding contribution of Rolf Loeber to developmental criminology. His salient paper on the future of the study of the age-crime curve (2012) is the focal point. AIMS: Follow some research trails that Rolf Loeber proposed in his 2012 paper. METHODS: Recent data on official offending from the Montréal Two Samples Four Generations Cross-sectional and Longitudinal Studies (MTSFGCLS) are analysed. The data were gathered for two generations of juvenile court males; five birth cohorts born around 1960 and followed from age 8 to 61, and five birth cohorts born around 1980, males and females traced from age 12 to 45. The age-crime curves are presented for the total prevalence. Epidemiological data are displayed for career descriptors: number of years active in offending, frequency, variety, onset, offset and duration. RESULTS: The age-crime curves of the two generations display the habitual shape reported in the literature. The epidemiological data shows that the population sample has a much lower curve in comparison to the court sample; this sort of difference is also observed between females and males. CONCLUSION: The difference between the two generations in the age-crime curves are interpreted in light of three evolutions in Québec from 1960 to 2000: (a) a radical change in the delinquency law, social and criminal justice policies, and treatment for juvenile delinquents; (b) a reduction of the juvenile and adult crime rates; (c) a significant increase in the wellbeing of the population on education, health and welfare services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it