Nodewise Predictability in Cross-Sectional Data Does Not Outperform Mechanical Totals in Predicting Sexual Reoffending
Bibliographic record
Abstract
This study compares the predictive accuracy of sexual reoffending using dynamic risk factors’ sum score (mechanical totals) and nodewise predictability, a model accounting for their interrelationships. Dynamic risk factors of North American men ( N = 5,315) were measured by the STABLE-2007. The area under the curve (AUC) of both methods was determined by splitting the dataset at a [20:80] ratio, repeated over 300 iterations with random training and test samples. Mechanical totals’ predictive accuracy outperformed nodewise predictability (AUC mechanical = 0.67, SD = 0.04; AUC nodewise = 0.50, SD = 0.03; t [299] = 80.2, Cohen’s d = 4.63, p < .001). This suggests that the conventional approach to predicting sexual reoffending is superior to a model considering dynamic risk factors’ interrelationships at the group level. Future research should explore whether nodewise predictability’s accuracy improves by incorporating temporal effects, subject variances, and centrality indices of individualized networks.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".