Food Security and International Migration: A comparative study of Asia, Middle East/North Africa, Latin America/Caribbean and Sub‐Saharan Africa
Bibliographic record
Abstract
International migration is a fast‐growing global phenomenon. It is influenced by various factors and the drivers are complex and varied. Research shows that migration can be triggered by poverty, food insecurity, inequality, poor income‐generating opportunities and increased competition for scarce land and water resources. Thus, for many, it is considered a necessary livelihood strategy. The main objective of this study was to explore the association between migration and food security status amongst various global regions, including Asia, Middle East and North Africa, Latin America Caribbean, and Sub‐Saharan Africa. Data collected face‐to‐face from the 2015 Gallup World Poll were used. The Food Insecurity Experience Scale within this dataset was used to categorize food security (food secure, mild food insecurity (FIS), moderate FIS, severe FIS). Different statistical analyses formed the basis this work. Firstly, descriptive statistics were used to analyze the data for the population migration at the regional level. Secondly, logistic regression was used to explain the relationship between migration status and food security status, adjusting for socioeconomic characteristics including, gender, age, education, employment status, the area of residence (rural/urban), born in the country or not, household size, and personal health status. Results from the adjusted logistic regression revealed that food security is negatively associated with international migration. More specifically, being severely food insecure increased one's likelihood of international migration by 1.414 times. Additionally, results showed that being male ( OR:1.283 ), being between 26 to 49 years of age ( OR:2.365 ), being divorced/living separately ( OR:1.397 ), being out of workforce ( OR:1.303 ), having higher education ( OR:2.133 ), having poor health status ( OR=1.505), living in urban areas ( OR:1.223 ) and having a large household size (OR:1.163) all significantly increased one's odds of international migration. Interestingly, being born in the country of origin decreased the likelihood of international migration (OR: 0.691) . Finally, with regards to global region, the probability of being an international migrant was highest amongst individuals from Latin America Caribbean ( OR: 2.101 ), and Sub‐Saharan Africa ( OR: 1.997 ). It is clear that among factors contributing to international migration, food security plays a key role. The findings are consistent with previous studies concerning international migration in that gender, age, education and region (urban/rural) were all associated with migration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".