The Canada-Caribbean Remittance Corridor : Fostering Formal Remittances to Haiti and Jamaica through Effective Regulation
Bibliographic record
Abstract
The World Bank has been at the global forefront in research on remittances. Studying over twelve bilateral remittance corridors thus far, the financial market integrity unit has focused its research on remittance market integrity issues and the specific incentives influencing the choices of channels to send money home. Initially conducted at the request of Department of Finance, Canada, this corridor, Canada-Caribbean, has clearly distinguished itself from other bilateral remittance corridors studied in the past. At the originating end of this corridor, these distinguishing features include a country that, throughout its history, has made immigration one of its primary social and economic building blocks. This corridor focuses on Jamaica and Haiti, two of the Caribbean's primary labor exporters and also the countries with the two largest Caribbean communities in Canada. Given the importance of remittances in the region, there is a need for effective, yet proportionate regulation. Risk must be effectively mitigated along potentially vulnerable routes, while innovation, competition and transparency in the remittance markets must be encouraged. Regulatory frameworks that reflect local conditions and are proportionate to the risks involved will facilitate the provision of services of the highest quality to migrants and their families. It is hoped that research provided from this study will generate policy dialogues among all relevant stakeholders, and assist national authorities in their efforts to effectively regulate and supervise the remittance markets. National authorities should continue to encourage the use of formal transfers and develop more reliable and competitive remittance channels. These channels must efficiently meet the varied needs of Caribbean migrant workers and their families in the safest and most secure environment possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".