Many Things to Many People: The Diversity of Motivations for Joining Diasporic Organizations in the Global South
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".