Is the increasing rate of unemployment a worst nightmare facing South Africa?
Bibliographic record
Abstract
Unemployment remains one of the major economic and social challenges facing both developed and developing countries. To date, it still remain a policy concern particularly in developing countries where lot of people go through long spells of joblessness, considerable loss of individual income and severe cut in standard of living. In South Africa, Unemployment has been unquestionably high particularly among the youth. This follows the publicized Stats SA quarterly labour survey of 2019 that revealed a 1.8 percentage points increase from 27.2% in the second quarter of 2018 to 29 per cent in the second quarter of 2019. Therefore, the primary motivation of this current study was to analyse the effects of increasing unemployment is South Africa with the aim of recommending possible solutions to the problem. The study employed annual time series data spanning the period 1989 to 2019. The results of the study revealed that GDP per capita, gross national income growth and literacy levels are negatively related with unemployment. This implied that increase in these variables would assist with unemployment reduction in South Africa.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".