Ethiopian Diasporans in South Africa: Dynamics of Migration, Opportunities, and Challenges
Bibliographic record
Abstract
Background/Contextualization Given the huge movement of people and growing interconnectedness in a globalized world, discourses around migration increasingly turn to the study of diasporas (e.g., Dinbabo 2022; Dinbabo and Badewa 2020; Dinbabo and Nyasulu 2015). The past few decades have witnessed growth in the quantities, variety, and activity of diasporas, as well as in diasporans’ investment in both their countries of origin and their host countries (IOM 2013). According to the IOM (2020, 2), as of 2020 the number of global migrants had reached an estimated figure of 272 million, representing about 3.5% of the global population. In addition, the global number of refugees and asylum seekers rose by about 13 million between 2010 and 2017, accounting for nearly a quarter of the growth in the number of all foreign migrants (IOM 2020, 2). A number of scholars argue that diasporans are significant actors not only in national, bilateral, and global matters but also in questions of migration and development relating to their countries of origin (Brubaker 2005; Demir 2015; Harald 2008; Peck 2006; Safran 1991). Indeed, a not inconsiderable amount of attention has been given to the roles that diasporas play in connecting community members and contributing to civil society organizations, self-help groups, development associations, government agencies, and the private sector (Belebema and Mensah 2017; Dinbabo and Carciotto 2015; Harald 2008; IOM 2013; Sithole, Tevera, and Dinbabo 2022). Diasporas play an essential part in social and economic development in both countries of origin and host countries (Peck 2006). Diasporas enable societies that are vibrant, ground-breaking, and open to international trade, investment, talent, and knowledge to develop these traits further (Harald 2008). Some diasporic communities participate in political activities that fuel animosities between communities and ethnic groups, in some cases funding insurgencies that cause havoc and destruction. Moreover, a number of diasporas have made it possible for nationalism, hostilities, and polarization to extend across nations and regions (Adamson 2005; Lyons 2007). Ethiopian diaspora groups in USA, Europe, Australia, and Canada, for example, have recently become engaged in nationalist movements within Ethiopia itself, spreading propaganda and fundraising on behalf of the Ethiopian political parties. This chapter studies Ethiopian diasporans in South Africa, asking how they have developed and what roles—if any—they play in matters in Ethiopia.
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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 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".