Economic Activities of Mining Production and Agricultural Economic Growth in South Africa
Bibliographic record
Abstract
South Africa is experiencing declining mining sector output that is economically detrimental as it leaves large numbers of mining workers unemployed.Unskilled retrenched mine workers from about 5,906 abandoned mines resulted in a discrete jump in the productive wealth of poor South Africans, as trends in mining profits declined.It is precisely this challenge that made economic succession planning in South African mines a potentially attractive policy option in the fight against poverty.This paper provides some of the first well-identified estimates of the viability of how post mining transformation can take place through agricultural production.Therefore, the paper aims to examine the relationship between the mining production economic activities and the agricultural economic growth using South African data.Employing the autoregressive distributive lag approach and impulse response functions, it has been found that the mining production has a significant long run relationship and can positively influence the agricultural economy.This is in line with the views of Rostow (1959) that mining production can be associated with agricultural activities and be used as a tool for post mining transformation.Therefore, it can be recommended that mines can engage to formulate policies that address post mining transformation into agricultural activities to redirect labor skills when the time of closing mines come.Suggested policies range from skill redirection of mine workers to agricultural activities.For instance, plantation of some fibrous plants that can grow well in mining land, and engage in some more economic activities like manufacturing and tourism of those agricultural products.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".