Changing Livelihoods and Landscapes in the Rural Eastern Cape, South Africa: Past Influences and Future Trajectories
Bibliographic record
Abstract
This paper seeks to understand the drivers and pathways of local livelihood change and the prospects for transformation towards a more sustainable future. Data are used from several studies, and a participatory social learning process, which formed part of a larger project in two sites in the Eastern Cape, South Africa. Secondary information from a wealth of related work is used to place our results within the historic context and more general trends in the country. Findings indicate that livelihoods in the rural Eastern Cape are on new trajectories. Agricultural production has declined markedly, at a time when the need for diversification of livelihoods and food security seems to be at a premium. This decline is driven by a suite of drivers that interact with, and are influenced by, other changes and stresses affecting local livelihoods. We distil out the factors, ranging from historical processes to national policies and local dynamics, that hamper peoples’ motivation and ability to respond to locally identified vulnerabilities and, which, when taken together, could drive households into a trap. We end by considering the transformations required to help local people evade traps and progress towards a more promising future in a context of increasing uncertainty.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".