Understanding the relationships between women's access to land resources and household food security in eastern Kenya
Bibliographic record
Abstract
Household food and nutrition security remains a major societal challenge, especially in the arid and semi-arid regions of sub-Saharan Africa.In smallholder farming systems, women play a critical role in improving household food and nutritional security as producers, food providers, and primary caregivers.However, women face multiple constraints in their access to land resources, limiting their livelihood potential.In response to calls to elevate women's access and control to agricultural resources, this dissertation seeks to better understand the relationships between women's access to land resources and household food and nutritional security.This research is set in the Kamba smallholder agricultural systems of Machakos and Makueni Counties in Kenya.It draws on theoretical concepts from the sustainable livelihoods approach, social capital, access theory, and social-ecological resilience.To better understand the formal and informal institutions governing land resource access in Kamba communities, I take a life stage perspective to identify how women navigate within local institutions to adapt to the shocks and stresses affecting their land resource access.Drawing on 77 key informant interviews, twelve focus group discussions (n=134), and eight community meetings (n=363), the findings reveal that women use a diverse set of relational access mechanisms to prepare for, and adapt to, land resource loss.The confluence of local gender norms and legal processes of land registration appear to constrain smallholder women from formal land ownership, although legal rights exist.The findings highlight that careful consideration of local institutions and gender norms is needed in national policy reforms that aim to promote women's access to land resources.By comparing life stages, I find emerging challenges that women in old age face in accessing land resources, particularly in cases of grandmothers raising their grandchildren in skipped-generational households.Employing a xiii 2.4 Results 2.4.1 Infancy, childhood, and adolescence 2.4.2Adulthood 2.4.2.1 Daughters and sisters 2.4.2.2 Wives 2.4.2.3 Mothers and land registration 2.4.2.4 Parents with unmarried and divorced daughters 2.4.2.5 Parents with married daughters 2.4.2.6 Divorces 2.4.3Old age 2.4.3.1 Parents and grandparents 2.4.3.2Widows 2.5 Discussion 2.5.1 Resilience thinking and land resource access in Kambaland 2.5.2Access strategies to land resources 2.5.3Life-stage perspective and gendered resource policies 2.6 Conclusion
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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 | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".