Eradicating Poverty and Unemployment: Narratives of Survivalist Entrepreneurs
Bibliographic record
Abstract
Researchers continue to argue that survivalist entrepreneurs remain the untapped source for improved socioeconomic development because they have the potential to create employment, and reduce poverty. Unemployment and poverty remain the biggest challenges for sub-Saharan Africa but specifically South Africa with an escalating unemployment rate. This is the basis for this study, which set out to provide an authentic insight into the lives of survivalist entrepreneurs in Cape Town, South Africa, for the purpose of revealing the reasons why they are unable to significantly grow and add substantially to the economy. A qualitative approach by way of personal interviews was followed so as to gain an in-depth understanding of the participants' stories. The findings suggest that survivalist entrepreneurs are able to assist in the socioeconomic development of an economy if appropriate support is given to them by government or through some public-private growth initiatives. This study contributes to survivalist entrepreneurship literature by specifically illuminating why, according to Statistics South Africa, survivalist entrepreneurs do not seem to create more employment opportunities, improve the economy and alleviate poverty. In acknowledgment of some of the study's limitations, we consequently advise that further study in this area may consider a combination of methods as well as other locations.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".