Digital Disruptions in the South Africa–Zimbabwe Remittance Corridor During COVID-19
Bibliographic record
Abstract
The impact of the COVID-19 pandemic on migrant remittances has generated a great deal of confusion and debate. This article aims to test three conflicting global and local narratives about the relationship between the pandemic and remittance flows in the South Africa–Zimbabwe remittance corridor. We refer to these as remittance pessimism, remittance resilience and remittance rerouting narratives. The article presents the pre-pandemic background context of migration from Zimbabwe to South Africa, the evidence for a shift from informal to formal remitting during the pandemic, and the implications of the remittance rerouting narrative for other corridors. We find that many Zimbabwean migrants in South Africa experienced severe economic impacts including unemployment, income loss and lack of access to COVID-19 relief measures. We conclude that there was a significant increase in formal, primarily digital, remittances during the pandemic and a decline in informal remittance conveyance. We highlight the need for more research in other remittance corridors to identify similarities and differences between them in terms of COVID-19 impacts and the shift from informal to formal remittances enabled by digital platforms.
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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 | 0.000 | 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".