EVALUATING THE MOTIVES OF INFORMAL ENTREPRENEURS IN KOFORIDUA, GHANA
Bibliographic record
Abstract
In recent years, there has been growing recognition in the entrepreneurship literature that many entrepreneurs operate in the informal economy and that not all these informal entrepreneurs are doing so out of economic necessity and because of a lack of choice. Instead, it has been asserted that some of these informal entrepreneurs choose to exit the formal economy and trade on an off-the-books basis more as a matter of choice. However, until now most research displaying this has been conducted in advanced western and post-socialist economies. Little has been written on whether this is also the case in third (majority) world countries. This paper starts to fill this gap by evaluating informal entrepreneurs' motives in sub-Saharan Africa. Reporting the results of face-to-face structured interviews with 80 informal entrepreneurs in Ghana, the finding is that the majority, especially the women informal entrepreneurs, are predominantly necessity-driven while those who are principally intentional participants in informal entrepreneurship are men. However, many women who initially entered informal entrepreneurship out of necessity have over time become more opportunity-driven entrepreneurs. The outcome is a call for wider research in other global regions on informal entrepreneurs' motives and whether similar gender variations prevail.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".