Remittances and homicides in Jamaica
Bibliographic record
Abstract
Purpose It has been suggested that homicides in Jamaica are partly driven by conflicts among criminals over funds coming from international lottery scams; most of these funds are channeled into the country via remittances. This study aims to determine the empirical relationship between remittances and homicides in Jamaica over the period 1985–2019. Design/methodology/approach The authors apply an error correction modelling framework while accounting for indicators of changes in socioeconomic conditions. Findings There are two. First, the authors find from impulse response analysis of the long-run dynamics that an increase in remittances is associated with an increase in homicides, and vice versa. Second, the authors find that there is bidirectional Granger causality between remittances and homicides in the short run. Social implications Two important implications are that policies should be strengthened to channel remittances to productive and legal investment opportunities and that greater efforts may be needed to stem the flow of funds coming from international lottery scamming and other illegal activities. Originality/value This is the first study that examines the dynamic relationship between remittances and homicides in Jamaica from a robust statistical perspective.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".