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Record W4404620127 · doi:10.1177/08997640241300515

Many Things to Many People: The Diversity of Motivations for Joining Diasporic Organizations in the Global South

2024· article· en· W4404620127 on OpenAlexafffund
Miriam J. Anderson, Madeline F Eskandari

Bibliographic record

VenueNonprofit and Voluntary Sector Quarterly · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsUniversity of TorontoToronto Metropolitan University
FundersSocial Sciences and Humanities Research Council of CanadaToronto Metropolitan University
KeywordsDiversity (politics)Economic geographyOrganization studiesSociologyPublic relationsBusinessPolitical scienceGeographySocial psychologyPsychologyAnthropology

Abstract

fetched live from OpenAlex

Small, grassroots organizations in the Global South play an increasingly prominent role in political advocacy and service provision for displaced populations. Literature on both diasporic organizations and civil society organizations has largely focused on those based in the Global North, however. This article examines the formation of a transnational women’s organization— Mouvement Inamahoro: Femmes et Filles pour la Paix et La Securité —by refugees following Burundi’s 2015 electoral crisis. We focus on the diversity of motivations for individual members in founding and joining it based on 68 interviews conducted in 2019 and 2021. We find that members’ geographical location (Global North vs Global South) played a role in the motivations for membership. Our findings suggest a form of diasporic organization currently untheorized, but one relevant to understanding the influence of displaced groups in local, regional, and international politics.

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 score0.437
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.277
Teacher spread0.257 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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