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Record W2996509528 · doi:10.5038/1911-9933.13.3.1682

Salutogenesis and the Prevention of Social Death: Cross-Cultural Lessons from Genocide-Impacted Rwandans and Indigenous Youth in Canada

2019· article· en· W2996509528 on OpenAlexafffundvenueabout
Jobb Arnold

Bibliographic record

VenueGenocide Studies and Prevention · 2019
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsCanadian Mennonite University
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Manitoba
KeywordsGenocideIndigenousKinshipPsychological resilienceSociologyEthnographyCriminologyGender studiesPolitical sciencePsychologyAnthropologySocial psychologyLaw

Abstract

fetched live from OpenAlex

Combining trans-disciplinary theories with cross-cultural ethnographic research, this paper explores community-based approaches to genocide prevention among Canadian-Indigenous groups as well as with Rwandan student genocide survivors. A Salutogenic framework is used to examine community responses to the micro-foundations of genocide (Antonovsky 1987). These processes are explored using first-hand accounts from “New Family” networks of student genocide survivors in Rwanda and members of a Canadian urban-Indigenous “Village.” These perspectives shed light on how locally adaptive, socially networked practices can help promote emergent forms of genocide prevention (Williams 1977). This paper focuses on three areas of local practice that have helped build meaningful community resilience: mutual aid, kinship, and ritual. These features contribute to salutogenic pathways that enhance group life and help communities to circumvent and supplant the day-to-day conditions that Claudia Card describes as social death (Card 2003).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.046
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0260.012
Scholarly communication0.0040.001
Open science0.0030.007
Research integrity0.0010.003
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 head0.051
GPT teacher head0.409
Teacher spread0.358 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2019
Admission routes4
Has abstractyes

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