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Record W4390057542 · doi:10.1093/jicj/mqac037

Should We Call for Criminal Accountability During Ongoing Conflicts?

2023· article· en· W4390057542 on OpenAlexaff
Ghuna Bdiwi

Bibliographic record

VenueJournal of International Criminal Justice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Law and Human Rights
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAccountabilityPunishment (psychology)PoliticsPolitical scienceHuman rightsLawCriminal lawCrimes against humanityCriminal justiceSociologyCriminologyWar crimeInternational lawPsychologySocial psychology

Abstract

fetched live from OpenAlex

Abstract This article suggests an account in the language of criminal law that merits the language for criminal accountability over the language of human rights, as a form of accountability, when prosecution is not possible. Calling for the prosecution of those most responsible for international crimes seems to be feasible after the war has ended, or at least when there is a vision for a political transition, but the war in Syria is ongoing and a vision for political transition remains elusive. The Syrian conflict has produced almost all kinds of heinous crimes, yet there is no clear political will to hold the alleged perpetrators of atrocity crimes accountable. At the same time, calls for criminal accountability in Syria, and discourse to achieve international criminal justice are taking place before the civil war ends. This article relies on the expressive theory of punishment to assess the rationales of calls for criminal accountability during the ongoing conflict in Syria. Out of many rationales, the article notes that calls for criminal accountability open the possibility of punishment and send a message of condemnation to perpetrators as well as a message of acknowledgment to victims. Furthermore, using the language of criminal accountability as a basis for the calls is stronger than using the language of human rights. The article discusses the problem of standing to call those responsible for international crimes to account and proposes that our shared humanity provides the authority for such calls while also pointing out limitations of this approach.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.123
GPT teacher head0.414
Teacher spread0.291 · 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 designTheoretical or conceptual
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

Citations1
Published2023
Admission routes1
Has abstractyes

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