Bibliographic record
Abstract
This presentation will give an analysis of the present situation in the South African taxi industry. This kind of semi-public and not-yet-industrialized transportation system also exists in other countries where infrastructures and public services are underdeveloped, but in South Africa, it has long been part of the struggles during the apartheid era. It used to be both a distinctive feature of the urban segregation and a weapon for all kinds of boycott campaigns.What has become of this system, ten years after the end of the official apartheid regime?The paper will first present the social, historical and political origin of this means of transport because minibus taxis cannot be understood without thinking of the large scale expropriation of the black population and the creation of black reserves for cheap labor around the cities. South African taxis and township life are entirely inseparable in the country.Using the example of a few boycotts in the apartheid era, we will then assess the needs and hopes of the population as regards to this question of transport. That will also help see if there were any changes since 1994: if there were none in the huge territorial segregation still prevailing in the country, there have been attempts to some restructuring of this informal sector of the economy. And that will bring us to a conclusion on what the present government is doing under the policy of recapitalization and Black Economic Empowerment.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.208 | 0.066 |
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".