Gender disparities in rural livelihood diversification and household food insecurity in northern Ghana
Bibliographic record
Abstract
Although income diversification is a well-established strategy for mitigating poverty and household food insecurity, gendered dimensions have been under-investigated. Examining a specific district in northern Ghana with heightened levels of food insecurity, we asked two questions: 1) Does income diversification reduce the risk of experiencing household food insecurity? and if so, 2) Does the relationship differ between women and men? This investigation used univariate, bivariate, and multivariate analysis of a cross-sectional survey of married spouses in 435 households. Results indicate that income diversification is linked with greater reporting of household food insecurity (OR = 1.23, p < 0.01). The link between livelihood diversification and household food insecurity is stronger among women than their husbands (OR = 1.27, p < 0.01). While we do not know whether diversified livelihoods cause food insecurity or food insecurity causes someone to diversify their income, improving the stability of income sources for both women and men in ways that consider gendered food dynamics is needed, particularly in rural African geographies disproportionally dependent upon biophysical environments and economies that are rapidly changing. African geographers and rural development professionals need to consider multiple individual and diverse gendered experiences for progressing scholarship that strives to explain and resolve uneven livelihood outcomes for household food security.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".