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Record W4307732862 · doi:10.1080/25729861.2022.2111106

Death and disappearance at border crossings: factualization devices and truth(s) accounts

2022· article· en· W4307732862 on OpenAlexaff
Paola Díaz, Anna Rahel Fischer

Bibliographic record

VenueTapuya Latin American Science Technology and Society · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Security and Public Health
Canadian institutionsUniversité du Québec à Montréal
FundersAgence Nationale de la Recherche
KeywordsNarrativeObjectificationRealmPoliticsPublic spherePolitical scienceValuation (finance)SociologyLawCriminologyArtBusiness

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0070.010
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.344
Teacher spread0.330 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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