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Record W3168524470 · doi:10.5038/1911-9933.15.1.1790

The Impact of Religious Beliefs, Practices, and Social Networks on Rwandan Rescue Efforts During Genocide

2021· article· en· W3168524470 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGenocide Studies and Prevention · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolitical Conflict and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsGenocideReligiosityCriminologyAction (physics)Collective actionPoliticsPolitical scienceSociologySocial psychologyPsychologyLaw

Abstract

fetched live from OpenAlex

In April 1994, in one of the most Christian nations in Africa, genocidal violence erupted culminating in the deaths of upwards of one million people. While thousands participated in mass killings, others choose not to, and rescued persecuted individuals instead. Relying on 45 in-depth interviews with individuals who rescued others in Rwanda, we demonstrate that religion is tied to rescue efforts in at least three ways: 1) through the creation of cognitive safety nets that enabled high-risk actions; 2) through religious practices that isolated individuals from the social networks of those committing the violence; and 3) through religious social networks where individuals encountered opportunities and accessed resources to rescue. The case of rescue in Rwanda illustrates how religiosity can support high-risk collective action, buffer individuals from recruitment to violent social movements, and can connect individuals in ways that enable them to save lives during extreme political 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.

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 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 score0.722
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.389
Teacher spread0.350 · 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