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Record W2164806301 · doi:10.1177/136346150003700306

Trauma Stories: Violence, Emotion and Politics in Somali Ethiopia

2000· article· en· W2164806301 on OpenAlexaff
Christina Zarowsky

Bibliographic record

VenueTranscultural Psychiatry · 2000
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsSomaliAngerSadnessPoliticsContext (archaeology)Political violenceSocial psychologyPsychologyDistressCriminologyPoison controlSociologyPolitical scienceMedicineClinical psychologyLaw

Abstract

fetched live from OpenAlex

This article draws on ethnographic material from Ethiopia to address two ways of making sense of violence and death: individual psychological trauma, in one society’s domain of psychological medicine, and personal and collective injury, in another society’s domain of politics. Both approaches entail theories of emotion and human nature. Both are embedded in social relations and have implications at individual, social and political levels. Among Somalis in Ethiopia, war-related distress is not interpreted in a medical framework aimed at healing. Rather, such violence is predominantly assimilated into the framework of Somali politics, in which individual injuries are considered injuries to a lineage or other defined group. The dominant emotion in this context is not sadness or fear, but anger, which has emotional, political and material importance in validating individuals as members of a group sharing mutual rights and obligations. Before advocating trauma-based models of war-related distress, researchers and practitioners should consider whether a medical framework would do better at helping individuals and communities to deal with distress and reconstruct meaningful lives and relationships in circumstances of longstanding collective violence.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.311
Teacher spread0.293 · 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.

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

Citations48
Published2000
Admission routes1
Has abstractyes

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