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Record W2037611733 · doi:10.1177/1477370812452087

Atrocity victimization and the costs of economic conflict crimes in the battle for Baghdad and Iraq

2012· article· en· W2037611733 on OpenAlexaff
John Hagan, Joshua Kaiser, Daniel Rothenberg, Anna Hanson, Patricia A. Parker

Bibliographic record

VenueEuropean Journal of Criminology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBattleTollCrimes against humanityPoliticsEconomic costPolitical scienceCriminologyHuman rightsWar crimeLawDevelopment economicsInternational lawEconomicsGeographySociologyMedicine

Abstract

fetched live from OpenAlex

Economic conflict crimes are defined in this paper as violations of international human rights and humanitarian law, as well as domestic law, associated with military and political conflict and producing significant monetary as well as other forms of suffering for civilians. Criminologists are well positioned by disciplinary emphasis to document and explain military and political violence resulting in economic conflict crimes. Criminal victimization associated with the US-led invasion of Iraq imposed an enormous toll on civilians. Yet there is little attention by criminologists or others to the profound economic costs to Iraqis, whether through lost property, life, or opportunities. We cautiously estimate that the economic losses for households in the city of Baghdad alone were almost US$100 billion, and more than three times this amount for the entire country, with Sunni groups experiencing significantly greater losses than others. So far as we know, our article presents the first estimates of civilian losses from economic conflict crimes that followed the US-led invasion of Iraq. These losses were widespread and systematic, the hallmarks of crimes against humanity.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.064
GPT teacher head0.282
Teacher spread0.217 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations31
Published2012
Admission routes1
Has abstractyes

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