DNA Databanks as a Source of Information about the Criminal Behavior of Individuals Who Have Been Linked to Crimes but Not Identified by Police
Bibliographic record
Abstract
Perpetrators of offences missing from police files limit the capacity to investigate criminal behaviour for criminological research and operational purposes. Recent studies have shown that forensic DNA databanks, which include samples of DNA not yet matched to an individual, have the potential to address this problem. By examining information associated with criminal cases that involved DNA matches, we demonstrate that individuals who cannot be identified through DNA differ from those for whom such identification is possible. Based on data from 19 years of DNA matches in Quebec, Canada, we were able to assess the co-offending and repeat offending behavior of unidentified and identified individuals as well as the diversification, level of severity, and types of offenses. We found that the crimes of the 1,448 individuals who had not been identified were marginal as compared with those of individuals who had been identified. Unidentified individuals were more likely to act alone in repeated crimes, to be involved in fewer cases, to use less violence, and to become more specialized with increased activity. Our results are consistent with other studies that demonstrate that the criminal activity of unidentified individuals accords with the exposure hypothesis. The association of these findings with a network analysis approach is innovative and could have a greater than expected impact on investigations and policies, as well as having implications for forensic intelligence.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".