Bibliographic record
Abstract
Purpose – This paper aims to examine the remittance behavior of Guyanese immigrants living in three communities of New York City, USA to assess their remittance behavior and if these are motivated by altruism or the intent to return to live in Guyana. Over the last two decades, remittances accounted for approximately 17 percent of the GDP of the Guyanese economy and continue to grow. The bulk of these remittances are significant from its native sons and daughters residing in the USA. Design/methodology/approach – This case study uses non-experimental survey research design with survey data collected from 300 participants living in New York, with 236 selected for analysis. Findings – Guyanese living in New York City remit monies to Guyana because of a pure altruistic motive as well as believing that their contributions have a positive impact on the economic development of their nations regardless of their intention to return to Guyana. These findings support the altruistic model on remittance motivation. Research limitations/implications – The data gathered for this survey are restricted to three communities in the USA where Guyanese are significant in numbers, thus limiting generalizations and findings to other countries such as Canada, England, where there are significant enclaves of Guyanese immigrants. Practical implications – New York-based Guyanese deem their remittances as contributing to the economic development of their country. This suggest that there may room for a coordinated policy on the part of the Government of Guyana to develop a coordinated plan to engage overseas-based Guyanese to remit more to help with Guyana economic development efforts. Originality/value – This is the first study to survey Guyanese in their host countries to gather information on remittances motivation and the perceived impact of these remittances from the sender's perspective. The paper highlights the significant remittance contributions of US-based Guyanese and their net private flows to Guyana.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".