Obstacles Faced by African Copreneurs in Black Townships of the Western Cape, South Africa
Bibliographic record
Abstract
The primary objective of this study is to examine the experiences of copreneurs specifically those who operate in the black townships of the Western Cape. Specifically, this study targets African1 copreneurs. Copreneurship is the term used when a husband and wife come together to own and manage a business as part of their daily engagement. As copreneurs, they confront specific obstacles. In South Africa, copreneurs exist within the framework of two fields, namely small, medium, micro business and family business. This study adopts a mixed research methodology, involving both quantitative and qualitative techniques. Thus the data collection instruments included a questionnaire and an interview schedule involving 150 subjects. The findings reveal, among other things, that major obstacles include the lack of finance for business expansion, as well as the lack of relevant information about government support, crime and absence of infrastructure. This study provides an opportunity to understand the specific challenges that c face, as well as information about the state of African copreneurship in black townships in the Western Cape. It is hoped that the findings and recommendations will shine a light on the specific challenges, as well as provide the necessary remedial opportunities so that these entrepreneurs are able to meaningfully add value to their respective communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".