Death and disappearance at border crossings: factualization devices and truth(s) accounts
Bibliographic record
Abstract
In this article, we analyze three forensic and counter-forensic devices that go beyond the strictly medico-legal realm to show which concrete practices and truth-spots contribute to (re)constructing a public account of migrant deaths and disappearances along border zones. Drawing on written documents and semi-structured interviews, we examine operations led by the International Committee of the Red Cross (ICRC) in Europe and Africa, the work of the Pima County Office of the Medical Examiner (PCOME) together with the NGO Colibrí Center for Human Rights in Arizona, and Forensic Oceanography's (FO) open-source investigations of shipwrecks of migrants in the Central Mediterranean Sea and the structural violence embedded in militarized border regimes. We argue that these practices constitute factualization devices, namely practices that transform lived experience of death and disappearance into an objectified reality that can be visibilized and mobilized in the public sphere as part of a counter-narrative. We demonstrate how these factualization practices are imbricated in valuation practices, i.e. practices that ascribe not only an epistemological but also an ethical, aesthetic and political value to the work of objectification, which inform the production of truth narratives.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.046 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".