The End of the ANC Era: An Analysis of Corruption and Inequality in South Africa
Bibliographic record
Abstract
ANC would always rule in South Africa, the African National Congress (ANC), which has been governing the country since the end of apartheid in 1994, received the worst results ever recorded. The ANC with president Jacob Zuma received 54 percent of the votes, which is a considerable decrease from 62 percent in 2011. This election was a clear sign that the ANC is in trouble towards the 2019 elections. The party seriously needs to rethink its strategies and investigates why the votes are decreasing. Given South Africa being a key player in global governance and in particular a strong leader among the African countries, it is significant to understand this political turmoil, as it may influence the political directions of other countries in that area. With reviews of relevant literature, therefore, this paper analyzes the current political situation in South Africa, focusing on corruption and inequality. The paper suggests connections between corruption, Jacob Zuma, and the potential end of the ANC era. The issues of inequality describes more the difficult situation that South Africans are facing and can be connected to the desire for change. It would be interesting to further analyze whether South Africa would be ready for a multiparty democracy with a peaceful transition of power after the national elections in 2019.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".