Forensic intelligence: Expanding the potential of forensic document examination
Bibliographic record
Abstract
Abstract Forensic document examination is characterized by its longevity, diversity, and evolution over time. Predominantly, published research within this field has focused on handwriting examination, the articulation of forensic conclusions, and the development of technical instrumental advancements, focusing on the use of document examination in the resolution of casework. This is a persistent and common problem within forensic science that Kirk identified in 1963 and that other authors have reaffirmed more recently. Ultimately, this has resulted in the potential of forensic intelligence, remaining relatively underexplored in the field of document examination. Forensic intelligence is a different way to view and analyze traces, shifting the focus from the traditional identification of source and activity, to instead identifying trends in criminal activity to assist in the reduction, prevention, and proactive disruption of crime. Despite a distinct disparity between these strands of research, there has been a persevering evolution toward the implementation of a systematic forensic intelligence method for the examination of fraudulent identity documents. Since its initial inception into the research community, this method has expanded and been implemented across Europe, and Canada, with tests also being conducted in Australia. These first tangible steps toward a forensic intelligence capacity within document examination have also inspired new work using forensic intelligence and systematic comparisons within the field of handwriting examination, as well as the recognition of the transversal potential of this method, with it being applied to both physical and digital documents. In this review, the fields of document examination and forensic intelligence will first be introduced, along with a subsequent examination of the research that has led to the creation of a forensic intelligence model within the field of document examination. It should be noted that this review has largely been limited to a review of research that has been published in English and French due to the language of the authors. This article is categorized under: Crime Scene Investigation > From Traces to Intelligence and Evidence Forensic Chemistry and Trace Evidence > Emerging Technologies and Methods Crime Scene Investigation > Epistemology and Method
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".