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Record W4400508625 · doi:10.1002/wfs2.1528

Forensic intelligence: Expanding the potential of forensic document examination

2024· article· en· W4400508625 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueWiley Interdisciplinary Reviews Forensic Science · 2024
Typearticle
Languageen
FieldComputer Science
TopicDigital and Cyber Forensics
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsHandwritingForensic scienceForensic examinationForensic identificationIdentification (biology)Crime sceneCriminal investigationIntelligence analysisComputer scienceField (mathematics)Data sciencePsychologyArtificial intelligenceCriminologyComputer securityForensic engineeringEngineeringHistoryArchaeology

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0030.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.310
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it