Improving the Effectiveness of the National DNA Data Bank: A Consideration of the Criminal Antecedents of Predatory Sexual Offenders
Bibliographic record
Abstract
This study assessed the effectiveness of the DNA Identification Act by examining whether 106 predatory sexual murderers and 85 predatory sexual assaulters had earlier convictions for offences that require offenders to provide a DNA profile to the National DNA Data Bank (NDDB). Offenders' criminal records were checked for convictions of primary and secondary designated offences, as stipulated by the act, and of non-designated offences that occurred prior to the murder or assault. A majority of the murderers (68%) and assaulters (59%) had no primary designated offence convictions; 50% of the murderers and 37% of the assaulters had no secondary designated offence convictions; and 39% of the murderers and 28% of the assaulters had no prior convictions for any designated offence. Overall, the largest number of prior convictions was for non-designated offences and the smallest for primary designated offences. Previous convictions for theft (non-designated) and breaking and entering (secondary) were most prevalent among the murderers and assaulters. Results suggest that the effectiveness of the NDDB for the identification of sexual predators may be improved by requiring mandatory provision of DNA samples following convictions for some non-designated and secondary designated offences.
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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 | 0.148 | 0.321 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".