Security in Uncertainty: Analysis of Climate Risks and Household Response to Food Insecurity in Northern Ghana
Bibliographic record
Abstract
Ghana represents an important case study for food insecurity research because of the high concentration of hungry populations in the country’s north relative to the south. While the increasing climate change and weather variabilities in north Ghana explain the region’s widespread hunger, there are also non-climatic factors that undermine households’ vulnerability. This dissertation explores smallholder farming households’ experiences with climate hazard events by emphasizing their vulnerabilities and response behaviours to climate and seasonality-induced food insecurities. It adopts a micro-level food systems lens integrated with livelihoods, vulnerability, and disaster risk theories. It is also informed by fieldwork and engagement with farming households in northern Ghana. A key argument of this dissertation is that food insecurity in northern Ghana is influenced by not only the climate-dependency of food system activities in the region but also the vulnerability in how food is produced, harvested, stored, and marketed. The findings reveal that climate change events lead to food crop productivity losses, cause damage to stored grain, and disrupt food prices, which affects food security. It also shows how households’ and food systems’ vulnerabilities intensify these climatic impacts and the concomitant variations in yearly food availability and access. The study further finds that poor households are not passive victims; they strategically adopt various actions to manage climate-induced food insecurity risks. In particular, the households’ responses follow a sequential order to preserve critical assets for current consumption needs and for the sustainability of agricultural livelihood. However, some of the response actions are associated with excruciating costs that could rebound and erode efforts for sustainable food security, especially for women and youth. Overall, the research provides evidence-based knowledge to address climate-related challenges for food security in the northern part of the country and to minimize regional disparities, which have long-term political implications. It also makes a strong case to draw attention to the diversity, sequence, and gendered nature of household responses to food crises.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".