Spaces of resilience, ingenuity, and entrepreneurship in informal work in Ghana
Bibliographic record
Abstract
Despite playing an important role in the economies of low-income countries, there is a perception that informal markets are haphazard and disorganized. Using in-depth interviews conducted in Accra, Ghana, this study examines the strategic choices that market women pursue to gain access to and thrive in informal working spaces and ensure long-term survival. The findings reveal that entry into the informal working spaces is contingent on women’s ability to forge and nourish ties with acquaintances, kinsmen and middlemen. Further, the study found that in contrast to the notion of unregulated competition typically associated with street vending, market relations among women traders in informal market spaces are marked by alliances between rival sellers that transcended religious, ethnic, linguistic, and generational divides. As well, a strict code of conduct governs market behaviour, underpinned by an ethos of cooperation and mutual assistance among rival sellers. Furthermore, market women in Accra articulate the rationale behind informal entrepreneurship in ways that align with local and national development agenda. In so doing, the market women lend legitimacy to their trade, demand accountability from local authorities, and oppose repressive practices by the state. We highlight the implications of our findings for city planning and development.
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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 | 0.014 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.006 |
| 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".