A comparison of diversity, frequency, and severity self-reported offending scores among female offending youth.
Bibliographic record
Abstract
[Correction Notice: An Erratum for this article was reported in Vol 24(3) of Psychological Assessment (see record 2012-04601-001). The article contained a number of errors which are corrected in the erratum.] Despite general consensus over the value of measuring self-reported offending, discrepancies exist in methods of scoring self-reported offending and the length of the reference period over which offending is assessed. This analysis compared the concurrent interassociations and longitudinal predictive strength of diversity, frequency, and severity offending scores measured over the past 6 months and diversity and severity scores measured "ever" between assessments. For violent offending, different scorings were highly correlated and equally predictive of adulthood offending. For nonviolent offending, there was significant continuity in diversity and severity-weighted diversity scores over the transition to adulthood but not in nonviolent frequency or severity-weighted frequency scores. Results support the use of offending diversity scores rather than offending frequency scores and highlight the importance of examining nonviolent and violent offending as separate constructs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".