Where are they? A review of statistical techniques and data analysis to support the search for missing persons and the new field of data-based disappearance analysis
Bibliographic record
Abstract
The disappearances of individuals are complex phenomena, spanning different regions and temporal periods. Evolving from different legal, social, and forensic disciplines, existing research has signaled the reasons for and contexts in which people disappear or go missing, as well as the development of investigative tools that assist, in fatal cases, in their identification. However, a different type of applied research, which we have labeled as data-based disappearance analysis (DDA), can offer statistical techniques to support the search for missing persons. In this paper, we review the literature on DDA, paying close attention to the evolution of this methodology and its contextual relevance. We highlight three applications by which DDA may support the search for missing persons: statistical inference, geospatial tools, and machine learning models and artificial intelligence. We demonstrate significant results using these applications, the potential misuses and ethical concerns, and draw lessons from their use. Lastly, we make recommendations to help researchers and practitioners support the search for missing persons.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".