Urban Informality and Migrant Entrepreneurship in Southern African Cities: 10â11 February 2014, Cape Town, South Africa
Bibliographic record
Abstract
The informal sector is the big story in African cities. To respond effectively, data collection and monitoring tools need dramatic improvement. Informal trading largely happens outside official city planning. This absence of recognition may be unconscious but is not benign. Ethnic networking and business positioning are of crucial importance for migrant-run small businesses. Those working in the informal sector in South Africa generally operate under hostile conditions. Volumes of trade and duties paid by cross-border traders show that this sector is significant to SADC governments. There is a policy contradiction between the government’s promotion of business tourism and the increasingly hostile attitude towards migrant entrepreneurs. Xenophobia can’t be ignored in debates around the way forward for informal entrepreneurship in Southern Africa. There is a need to look at how to assist South African traders in ways that are not discriminatory, unlawful and do not ignore the interests of wider parties. Zimbabwe’s informal economy is the country’s major employer. Also, the movement of remittances between South Africa and Zimbabwe is a large industry. There is an undercurrent of globalization in Maputo’s markets, with used clothing from the Global North sold by Indian traders to Mozambican market traders. Africa has the poorest and least educated of the overseas Chinese diaspora. Chinese traders have succeeded with small businesses in Africa where local firms have failed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".