No. 74: Informal Entrepreneurship and Cross-Border Trade between Zimbabwe and South Africa
Bibliographic record
Abstract
Informal cross-border trading in Zimbabwe has become more than a survivalist strategy and should be seen as an important pillar of the country's economy. This report, part of SAMP’s Growing Informal Cities series, seeks to provide a current picture of informal cross-border trading in Zimbabwe and provides detailed insights into the activities of traders from the capital, Harare, who travel regularly to Johannesburg, South Africa, as part of their business. The traders make a monthly profit that far exceeds the salaries of most Zimbabweans in formal employment. Furthermore, many traders have been able to grow their businesses to such an extent that they hire people from outside their families. In Zimbabwe, this trade remains a female-dominated activity and traders are generally well educated and relatively young. Almost all respondents interviewed had started their businesses in the post-2000 era. Most had never held a formal job and went into informal cross-border trading either because they were unemployed or already involved in informal sector activities in Zimbabwe. This report notes important contributions these traders make to both the Zimbabwean and South African economies. The contribution of the informal economy in generating jobs and reducing unemployment needs to be acknowledged in Zimbabwe by policies that encourage rather than restrict the operation of informal 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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".