Migrant Women’s Food Insecurity Experiences in the Breadbasket of Ghana
Bibliographic record
Abstract
Abstract In response to rapidly deteriorating climatic conditions and the resulting low agricultural productivity, migration has become an important safety net for smallholder farmers in Africa. In semi-arid northern Ghana, especially the Upper West Region (UWR), many people migrate to rural farming communities in the Middle Belt of the country—popularly referred to as Ghana’s breadbasket—to meet their food security needs. In recent times, there has been an increase in the participation of women in these migration patterns. Despite this, many studies on migration and food security in Ghana continue to focus on the experiences of households or male migrants, creating a lack of knowledge about the experiences of food insecurity in migrant women. Food insecurity was measured using a modified version of the Household Food Insecurity and Access Scale (HFIAS). Using a cross-sectional study design and Ghana as a case study, this chapter employs generalised linear latent and mixed models to examine the determinants of the food insecurity experiences of migrant women with an emphasis on length of stay. The findings demonstrate that even after migrating, women face several barriers that continue to predispose them to food insecurity, including lack of social support and autonomy. Given the general lack of empirical evidence on the food security experiences of women migrants, this analysis is positioned to generate insights on the correlates of food insecurity among women migrants and, more broadly, the efficacy of migration as a fallback strategy for navigating food security among women. In the context of increasing climate variability and the associated disproportionate impacts on marginalised groups, particularly women, this study generates insights into development policy in Ghana and similar contexts within the Global South.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".