“An Be Jigi”: Collective cooking, whole grains, and technology transfer in Mali
Bibliographic record
Abstract
This paper addresses how available resources, food security, technology, and culture are shaping the choices rural Malian women are making to ensure the health, energy, and well-being of their families. This research contributed to evaluating an eight-year research project (An Be Jigi) targeting improved nutrition. The study, performed over four months, used semi-structured interviews of 120 women in six villages in Mali to assess the identified issues with qualitative and quantitative approaches. This paper describes the history of the An Be Jigi project, whole-grain processing techniques, and group cooking for knowledge sharing with rural women for improved nutrition. Interviews revealed substantial adoption of whole-grain processing techniques and women’s appreciation of the nutritional benefits of those techniques. The women engaged in group cooking ( cuisines collectives) appreciated the activities and mentioned multiple benefits from using them. Women identified access to mills, and to some extent the social stigma of laziness and poverty associated with whole-grain food, as limiting factors of adoption. This study of women’s practices and perceptions regarding use of whole grain tells a story of changing consumption habits being shaped by culture, technology, knowledge, and available resources. Malian women are agents of change and care in their adoption of new techniques and recipes for the improved nutrition of young children and households.
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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 | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".