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Record W3024728855 · doi:10.5334/sta.740

‘Is Help Coming?’ Communal Self-Protection during Genocide

2020· article· en· W3024728855 on OpenAlexvenueno aff
Deborah Mayersen

Bibliographic record

VenueStability International Journal of Security and Development · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Peace and Security Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsGenocideTypologyNormativeArmenianPolitical scienceResponsibility to protectSociologyLawCriminologyInternational lawHistoryAnthropology

Abstract

fetched live from OpenAlex

Despite the rhetoric of the Responsibility to Protect principle (R2P), vulnerable groups continue to experience genocide. Some, such as the Yazidis in Iraq, have tried to mitigate genocide through communal self-protection. The dominance of R2P in contemporary normative discussions about responding to genocide, however, means that there has been a lack of research into the lived realities of such experiences. This article explores the phenomenon of communal self-protection during genocide, through a multiple case study analysis. It examines the pre-eminent examples of communal self-protection during three cases of modern genocide — the experiences of the Armenians at Musa Dagh during the 1915 Armenian genocide, the Tutsi at Bisesero during the 1994 Rwanda genocide, and the Yazidis in Sinjar during the 2014 Yazidi genocide. It presents a typology of communal self-protection strategies during genocide, developed from the case study analysis. The article finds that communal self-protection is only feasible as a strategy in exceptional circumstances. Even in a best-case scenario, communal self-protection offers a temporary reprieve, rather than sustainable living conditions. Vulnerable groups attempting communal self-protection are ultimately reliant on external rescue for their survival, which may not be forthcoming. Communal self-protection should therefore not be regarded as a viable strategy to mitigate genocide in any circumstance.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score0.434

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 studies0.0000.000
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 head0.038
GPT teacher head0.288
Teacher spread0.250 · 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 designQualitative
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

Citations5
Published2020
Admission routes1
Has abstractyes

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