A Hybrid Model to Alleviate Unemployment and Poverty in South Africa
Bibliographic record
Abstract
Poverty and unemployment are considered social threats in South Africa as the rate keeps on escalating while few measures are implemented to alleviate the trend. This study devised a hybrid model to reduce the rate of poverty and unemployment in South Africa. The Human Capital Theory formed the theoretical base of this study, which explained the need for the government to invest in education to improve the chances of gaining employment to reduce poverty. The study adopted a quantitative approach and data were collected from only secondary sources. Major findings disclosed that the poverty rate in South Africa is at 49.2% while 64.2% of South African blacks remain poor. The study revealed that the unemployment rate is at 30.1% in the first quarter of 2020 while provinces such as Eastern Cape (40.5%) and the Free State (38.4%) have the highest share of unemployment in the country. The meta-analysis conducted revealed that improvement is needed in areas such as legislation and labour laws, entrepreneurial development, youth development policies, common vision and leadership, sectoral development, business climate, acquisition of skills and education, engagement management, and strategic management. This is in an endeavour to reduce poverty and unemployment rate in South Africa.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".