No. 42: Remittance Practices, Digital Technologies and the Ghana-Canada Migration Corridor
Bibliographic record
Abstract
Migrant remittances have been recognized as vital resources for the well-being of recipient households and communities, as well as for sustainable development in the Global South. However, these flows can be impeded by limitations in the infrastructure, financial systems, and regulatory environments of both sending and receiving countries, as exemplified by the high costs associated with remitting to sub-Saharan Africa. Consequently, the use of less secure but more affordable informal remittance channels persists. Driven by the rapid growth of fintech technologies in recent years, including mobile money and web-based platforms, the digitalization of remittance-sending and receiving processes has the potential to rectify some of these challenges. By reducing transaction costs and improving the speed and transparency of transfers, digital remittances can contribute to financial inclusion and economic development in recipient countries. The perceived changes in remittance practices brought about by new digital technologies warrant a detailed examination of individual migration corridors. This paper presents a case study of the Ghana-Canada migration and remittance corridor, assessing the uptake of digital remittances and identifying existing limitations, particularly about remittance prices. This understudied corridor is characterized by increased migration flows, growing immigrant communities with strong transnational linkages, and high participation in remitting processes, despite barriers to the socioeconomic integration of racialized immigrants in Canada. The dramatic growth of the mobile money system in Ghana is another key aspect of these digitalization processes and modifications to remitting practices.
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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 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".