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Record W2802258063 · doi:10.1093/hgs/dcy018

Representing Mass Violence: Conflicting Responses to Human Rights Violations in DarfurJoachim J. Savelsberg

2018· article· en· W2802258063 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHolocaust and Genocide Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHuman rightsCriminologyPolitical scienceLawPsychology

Abstract

fetched live from OpenAlex

Representing Mass Violence, by sociologist Joachim Savelsberg, examines how the Western world portrayed the mass violence in the western Sudan region of Darfur during the early twenty-first century. The study is based on in-depth interviews with relevant agents such as international legal specialists, humanitarian aid workers, diplomats, and journalists, as well as on an ambitious quantitative analysis of more than 3,300 North American and Western European newspaper articles. To frame his topic Savelsberg employs Kathryn Sikkink’s notion of a “justice cascade,” which posits that the increase in international prosecution for human rights abuses over the past two decades has had a significant political impact. He also uses Pierre Bourdieu’s concept of a “social field” in which agents operate according to a distinct worldview. The subjects of Savelsberg’s study work in their own specific fields, a fact that shapes their opinions and expressions but also allows for improvisation. Savelsberg also employs intersectionality, demonstrating how interconnected variables such as educational background, nationality, and gender influenced actors.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.058
GPT teacher head0.420
Teacher spread0.361 · 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