Transnational Family Ties, Remittance Motives, and Social Death among Congolese Migrants: A Socio-Anthropological Analysis
Bibliographic record
Abstract
This article investigates how transnational familial links and socio-cultural dynamics shape migrants’ remitting behavior and inform their relationships. It shows that most research on remittances fails to capture migrants’ personal and social significance of remittances embedded not only in their transnational social relations, but also in cultural contexts. Drawing on new empirical qualitative research amongst Congolese migrants in South Africa, the article argues that migrants remit primarily in a bid to escape social death by fostering familial belonging and sustaining social status. It finds that socio-cultural influences and internalized social stereotypes about economic effects of emigration shape migrants’ awareness of their role expectations in communities of origin. These role expectations exercise on them such a social pressure that migrants often feel a compelling need to be perceived as financially “successful” as well as “valid” and “good” family members not only in their communities of origin but also among other migrants. As such, remittances become fundamentally the measures and criteria shaping migrants’ sense of belonging and social and familial inclusion or exclusion. In this sense, for individual migrants, remittances play an essential instrumental role portraying such images and at the same time are seen as a means to avoid social stigmatization and exclusion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".