No. 04: Canadian Support for Women in the Informal Food Sector in the Global South
Bibliographic record
Abstract
The COVID-19 pandemic has exacerbated the vulnerabilities faced by women in the informal food sector in the Global South, deepening existing gender inequalities and economic inequities. Women informal food vendors play a critical role in ensuring food security in urban areas, yet their contributions are often undervalued and unsupported by formal policy frameworks. This policy brief presents two case studies from Mexico City and Maputo, Mozambique, highlighting the challenges women face in this sector, including lack of legal security, financial instability, and limited access to social protection. In response to these challenges, there is a pressing need for targeted interventions to support women in the informal food sector. Canada’s Feminist International Assistance Policy (FIAP) offers a unique opportunity for Canada to align with global efforts, such as the UN Women Feminist Plan for Sustainability and Social Justice, to promote gender equality and economic empowerment in the post-pandemic recovery. This brief recommends short-term actions, including the enhancement of gender-sensitive pandemic response measures and the extension of development assistance to informal food enterprises. In the longer term, it calls for strengthening partnerships, addressing rural biases, and empowering women in rapidly urbanizing areas. [This policy brief was developed by the authors without the assistance of AI.]
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.002 |
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".