South Africa-BRIC-SADC Trade Alliances and the South African Economy
Bibliographic record
Abstract
The article discusses the implications of South Africa-Brazil Russia India China-Southern African Development Community (BRIC-SADC) trade alliances on South Africa’s economic growth. The analysis follows the periods in which South Africa is mired by fluctuating exchange rate and rising cost of living, as denoted by the rising consumer price index (CPI). In order to understand the implications, an autoregressive redistributive modelling (ARDL) was utilized on quarterly data from 2005 quarter 1 to 2017 quarter 3, regressing South Africa’s growth against South Africa-BRIC and South Africa-SADC trade balances, the main variables of interest. The empirical results identify a significant long-run relationship of the selected variables. However, the results review a negative contribution of South Africa-BRIC trade on South Africa’s economy, while the South Africa-SADC trade produced positive results. Trade composition remains a major challenge for South Africa-BRIC trade. Continued innovation and research and development will shift reliance on primary commodities for exports to mechanized products, hence increasing gains from the lucrative BRICS trade and the non-utilized SADC trade.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".