The Storm of Poverty Reduction Strategy in Africa: Chronology of Experiences from South Africa
Bibliographic record
Abstract
The paper present the trend of government programmes and interventions used in tackling the endemic problem of abject poverty. The pluralistic nature of these programmes is encouraged by the dynamic socio-economic circumstances within South Africa domain. Concerns raised from “Towards a Ten Year Review” emphasised the necessity to harmonize all Government’s development programmes under a single structure or “Social compact”. In view of the importance of agriculture, particularly for developing countries, a large aspect of the development discourse around agriculture has been focused on poverty alleviation. Across all nine provinces, the noticeable and critical problem is poverty which the government needs to contend with and address. The challenges faced presently in the reduction of poverty are numerous. Key programme responses of the South African government considered in this paper are: Legal context, Reconstruction and Development Programme (RDP), Growth, Employment and Redistribution (GEAR), Local government transformation, Land reform programme, Skill support and Development Programme, Farmer support and Extension services, Support for emerging farmers, National Public Works Programme (NPWP), Integrated Sustainable Rural Development Strategy (ISRDS), Social grants and unemployment, Education and poverty reduction, Agriculture Black Economic Empowerment (AgriBEE), Provincial Growth and Development Strategy (PGDS), Local Economic Development (LED) and Donors’ in poverty alleviation through Official Development Assistance (ODA).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".