An Intersectionality-Based Policy Analysis (IBPA) of Post-Pandemic Recovery Policies: Experiences of Women Informal Food Vendors in Kisumu City, Kenya
Bibliographic record
Abstract
Introduction: The informal food sector in Kisumu City, largely run by women informal food vendors, plays a crucial role in the urban food system. However, these female-led businesses faced disproportionate risks stemming from COVID-19-related policies, exacerbating gendered vulnerabilities. This paper explores the gender gaps of post-pandemic recovery strategies and their implications for resilience, recovery, and sustainability of women-led informal food businesses. Methods: This cross-sectional study was guided by the Intersectionality-Based Policy Analysis (IBPA) framework. In collaboration with the Pamoja Community-Based Organization, we employed qualitative methods grounded in community-based participatory approaches. Data were collected through key informant interviews (n = 20), depth interviews (n = 20), focus group discussions (n = 40), and a review of policy documents (n = 2). Data was analyzed guided by the eight principles of the IBPA framework alongside Braun and Clarke’s six-phased thematic analysis approach. Results: Findings indicated that power dynamics in the formulation of post-pandemic policies and top-down implementation approaches excluded women informal food vendors from meaningfully participating in policy processes. For example, female vendors were excluded from the recovery priorities as the strategies adopted had limited to no targeted gender-responsive interventions. As such, women informal food vendors faced several challenges during recovery, including limited government support, barriers to accessing credit facilities, heightened household and unpaid care work, gender-based violence, sexual exploitation, and insecurity. The female vendors employed both individual agency and collective action to facilitate recovery. Discussion: Gender-responsive COVID-19 policies were critical to addressing the disproportionate impact of the pandemic on women-led informal food businesses. Moving forward, a comprehensive understanding of existing sociocultural inequalities is crucial for designing post-pandemic strategies that are gender-inclusive and promote equitable recovery. Such an approach would enhance women informal food vendors’ resilience to emergencies and their contribution to urban household food security and livelihood.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".