Microscopic Examination of the New South African Economic Reconstruction and Post-Pandemic Recovery Plan
Bibliographic record
Abstract
South Africa is certainly not the only country affected by the novel Coronavirus pandemic in Africa – though it currently stands as the worse-hit country by the virus, but it is presently on the list of some of the countries that defiantly take advantage of the rare opportunity imbedded in the adversity of the pandemic to reset the economic landscape. The country is promptly on route to a recovery that is one-of-a-kind in the continent, from a pandemic that saw half of its GDP depleting within months. In one of his regular situational updates to the nation, President Cyril Ramaphosa announced a comprehensive Masterplan with which South Africa aims to swim against the tide of the recession caused by Coronavirus pandemic. The midmonth announcement which upheld the remaining quarter of the hapless year 2020, unveiled the new recovery Plan for the nation known as THE SOUTH AFRICAN ECONOMIC RECONSTRUCTION AND RECOVERY PLAN (ERRP). Whilst the President and the people preciously revel the new ERRP and the much-anticipated reconstruction of the economy of the country in order to address widening inequality now relapsed by the pandemic, we painstakingly examine it in detail to divulge the numerous “new” effects that inundate the decadal Plan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".