Trade and Women’s Economic Empowerment: Qualitative Analysis of SMEs from Ghana, Madagascar, Nigeria, and Senegal
Bibliographic record
Abstract
Abstract Using Ghana, Madagascar, Nigeria, and Senegal as case studies, this chapter elucidates the dynamics of trade and women empowerment in Africa through a qualitative analysis that involved focus group discussions (FGDs) and key informant interviews (KIIs) with managers and employers, and employees of SMEs, as well as government officials and Trade Support Organizations (TSOs). After presenting the operational context of trade and economic performance in the four candidate countries, the barriers to women’s participation in trade are discussed using the PESTLE framework. Our qualitative analysis shows that cross-cutting factors that influence job creation for women across the trade sectors include the nature of work, job demands in terms of physical strength and timing, and working conditions of employees. The chapter argues that trade has the potential to empower women. However, if finds that various challenges prevent women from maximizing the gains from trade and proposes that a strong public–private partnership is necessary for trade to lead to women empowerment.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".