Livelihood profiles and adaptive capacity to manage food insecurity in pastoral communities in the central cattle corridor of Uganda
Bibliographic record
Abstract
Adaptive capacity is the capabilities, resources and institutions of a country or region to implement effective adaptation measures. This article aims to highlight pastoral communities’ differential adaptive capacity to buffer household food insecurity. We use mixed methods including case households and key informants to provide qualitative data on determinants of adaptive capacity. Subsequently cluster analysis is applied to combine survey data from respondent households on the basis of the livelihood capitals. Three distinct, heterogeneous livelihood profiles are identified. The Minimally-endowed face uncertain access to livelihood capitals; Large-herd Landlords are endowed with physical and financial capital – ownership of land and large numbers of livestock; while the Land-rich are endowed with natural capital – access to large sizes of land. This denotes different types of adaptive capacity and underscores the need for agricultural extension, technology transfer and other interventions to be differentiated based on the variance in adaptive capacity and challenges of the existing heterogeneous livelihood clusters. We argue that if such differences are not first identified, development strategies including those of agricultural extension could fail in their attempts to ensure sustainable household food security. Rather than being a homogenous community, pastoralists in the central cattle corridor of Uganda belong to three heterogeneous livelihood profile clusters. Each cluster is differentially endowed with livelihood capitals which denote different types of adaptive capacity. As an empirical study done at household level, this work contributes insights that can be considered in designing and undertaking studies of other rural communities, prior to planning and execution of interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".