MétaCan
Menu
Back to cohort
Record W3197606494 · doi:10.1111/imig.12920

Migration, resettlement and integration of survivors of the 1994 genocide against Tutsi in Rwanda in Canada: A community‐based study

2021· article· en· W3197606494 on OpenAlexafffundabout
Sophie Yohani, Linda Kreitzer

Bibliographic record

VenueInternational Migration · 2021
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersPolicyWise for Children and Families
KeywordsGenocideDiasporaFamily reunificationThematic analysisImmigrationPolitical scienceCommunity integrationEconomic growthGender studiesCriminologyGeographySociologyQualitative researchMedicineAnthropologyLaw

Abstract

fetched live from OpenAlex

Abstract This article describes the migration, resettlement and integration challenges and strengths of members of the African Diaspora in Canada who identify as survivors of the 1994 genocide against the Tutsi of Rwanda. Data were generated from semi‐structured interviews with 16 adult community members and a thematic analysis conducted inductively and collectively with the research team consisting of academics and representative community members. This article provides insights into the unique long‐term impacts of genocide on migration, resettlement and community‐level functioning for this group of African migrants living in a mid‐Western city in Canada. Results highlight how Canadian immigration policies limit migration options and prevent family reunification for migrants with none or few remaining family members and the associated resettlement challenges experienced by this group. Results also show the vital role the Rwandan Diaspora community, and particularly other survivors, play in supporting resettlement, integration and overall well‐being of genocide survivors.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.080
Threshold uncertainty score0.416

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 head0.034
GPT teacher head0.322
Teacher spread0.288 · 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 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

Citations1
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueInternational MigrationSame topicMigration, Health and TraumaFrench-language works237,207