Les applications de téléphones intelligents et tablettes pour l'investigation de scène de crime: état des lieux, typologie et critères d’évaluation
Bibliographic record
Abstract
In recent years, applications for smartphones and tablets have undergone important developments, and their use is gradually becoming a new standard in everyday life and in some professional circles. Forensic science, and especially crime scene investigation, will not be an exception. However, should we expect a revolution of methods and practices, or only an extension of available tools without any fundamental change?To address this question and establish the current state of research and practice, this study considers a literature review, semi-structured interviews, and a survey of forensic unit representatives in Canada and Switzerland. It appears that there is at present no specific policy or framework to guide the development, use, and evaluation of applications that could support the investigation of crime scenes. Therefore, this article proposes a typology of applications and criteria for assessing their relevance, reliability, and response to operational requirements. The detailed study of five applications is used to illustrate the assessment process.A better understanding of issues and critical success factors associated with the use of applications is necessary to ensure the measured and intelligent integration of this technology in the daily investigation of crime scenes. In this regard, strengthening of scientific and pragmatic research that considers operational constraints is considered desirable.
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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 | 0.035 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".