Forensic intelligence: Expanding the potential of forensic document examination
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,003 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle