Mitigating the risk of military personnel becoming unaccounted for on the battlefield: An interview with Stephen Fonseca and Vaughn Rossouw on the ICRC’s Military Personnel Identification Project
Bibliographic record
Abstract
Stephen Fonseca has been involved in medicolegal death investigation for over twenty-seven years, with a focus on forensic human identification and on education and professional development in forensic investigations and emergency management. He has co-authored several articles on forensic human identification. As a coroner in Canada, he led the development of a provincial forensic identification system to concurrently address recently reported and historical unsolved identification cases and managed the Identification and Disaster Response Unit (IDRU) in British Columbia for eight years. The IDRU was regionally recognized for the implementation of the multidisciplinary Identification Information Management Model, for which Stephen received the Queen Elizabeth II Diamond Jubilee Medal. He joined the International Committee of the Red Cross (ICRC) in 2013, initially working in the Middle East and thereafter supporting authorities primarily in Africa and globally with the implementation of well-functioning medico-legal systems and improvement of forensic identification in peacetime as well as during and after conflict, disasters and migration. He is currently the Head of the ICRC’s African Centre for Medicolegal Systems, a satellite hub of the Central Tracing Agency’s (CTA) Advisory Red Cross and Red Crescent Missing Persons Centre. Stephen leads the ICRC’s global Military Personnel Identification (MPI) Project, working with State armed forces around the world to develop global guidelines and implement measures to reduce the number of military personnel who become unaccounted for. Vaughn Rossouw is an admitted Advocate of the High Court of South Africa. He holds an LLB and an LLM (cum laude) in public international law from the University of Pretoria, specializing in international humanitarian law (IHL) and human rights in military operations. Vaughn joined the ICRC in 2022 as Legal Adviser to the CTA’s African Centre for Medicolegal Systems, advising State authorities and stakeholders in the forensic science services sector on the applicable international and domestic legal frameworks with respect to missing persons and their families, and on the dignified management of the dead in armed conflict and other situations of violence. Vaughn was featured in the Review ’s “Emerging Voices” issue of 2021 and has co-authored publications on transnational investigative approaches to address missing and deceased migrants in Southern Africa together with colleagues at the ICRC Pretoria Regional Delegation.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.025 |
| 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".