An exploration of unsolved missing persons cases suspected of a criminal outcome: A forensic victimology approach
Bibliographic record
Abstract
Abstract The purpose of this study is to explore cases of missing persons suspected of being criminal. Specifically, this research aims to empirically describe the circumstances surrounding criminal disappearances and examine whether there are different subcategories within these cases using a victimological framework. The data used in this study come from an operational police database. The sample includes 155 unsolved missing person cases whose thorough investigation by the police concluded that a criminal motive remained the most likely hypothesis. These cases occurred on the entire territory of metropolitan France and overseas. Multidimensional scale analysis was used to explore the context of disappearance according to missing person characteristics, lifestyle, everyday activities, and situational exposure at the time of the disappearance. Results show that the distribution of variables in a two‐dimensional space reveals four distinct different categories related to lifestyle and situational exposure: riskier lifestyle, riskier situation, safer situation, and safer lifestyle. This study is the first to empirically explore unsolved missing person cases suspected of a criminal outcome. Moreover, the framework used in this study demonstrates the importance of victimology in an investigative context.
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.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".