MEGA <sup> <i>♪</i> </sup> —Empirical Support for Nomenclature on the Anomalies
Bibliographic record
Abstract
Applied are empirical findings supporting the authors' previously presented nomenclature identifying two subsets of sexually abusive youth overlooked by most contemporary risk assessment tools: sexually violent and predatory sexually violent youth. The cross-validation findings on an ecologically framed risk assessment tool, MEGA (♪) (Multiplex Empirically Guided Inventory of Ecological Aggregates for Assessing Sexually Abusive Children and Adolescents [Ages 19 and Under]) (N = 1,056 male and female sexually abusive youth, ages 4-19, including youth with low intellectual functioning), from the United States, Canada, England, and Scotland, were utilized. Findings provided normative data, with cutoff scores according to age and gender. Most contemporary risk assessment tools have three levels (low, moderate, and high), which may in fact be limited in assessing the range of risk level. The MEGA (♪) cross-validation established a new range of risk level, with the fourth level (very high) definitively identifying the most dangerous youth, thus empirically supporting the nomenclature of sexually violent and predatory sexually violent youth.
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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 | 0.025 | 0.134 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.005 |
| 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".