Informal Channel: An alternative for Remittances and International Money Transfers between China and African Countries
Bibliographic record
Abstract
Foreigners in different countries rely on different remittance channels for sending and receiving money. This paper analyses the privileged remittances channels that foreigners use in China to send money to their home country and to receive money in China. This paper explores foreigners’ choices of remittance channels in China and the reasons for their choices. On the other hand, the paper aims to highlight the barriers and difficulties foreigners in China experience when sending and receiving money through a formal institution. To the best of our knowledge, this paper is the first to investigate the remittance channels used by foreigners in China; this is the innovation of this study. Using a questionnaire, we collected data from 105 foreigners living in different cities in China; and used descriptive statistics to analyze the data. Findings show that foreigners in China willing to use formal remittances channels face several barriers and therefore rely on informal channels to carry out their transactions of sending and receiving money. To render the formal remittances channels accessible for every foreigner, banks and formal Money Transfer Operators (MTOs) in China should promote English service for foreign customers and deal with computer system problems that reverse names and surnames. In addition, they should deal with the problem of restrictions for some nationalities, reevaluate the transaction amount limits, and offer less complex administrative procedures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".