Role of women in the value chain of insects used as food and feed in Africa
Bibliographic record
Abstract
Abstract In Africa, insects have long been used as human food and animal feed. This practice is presently being promoted to reduce malnutrition and provide new opportunities for the economic development of the rural poor, in particular women. However, gender issues need to be considered at an early stage and, to set up a baseline for such considerations, it is essential to understand the role of women in all present aspects of the sector. This paper reviews the role of women in the collection, production, processing, marketing and consumption of insects used as food and feed in Africa. Women tend to dominate the value chain although their role varies with insects and regions. Most insects used as human food are still field collected, usually by women, who also lead marketing activities, especially when the market is of low value. Women also prepare and cook the insects. Consumption is usually shared among men, women and children, but gender balance may vary with insects and regions and may be influenced by traditions and taboos. There is less information available on gender roles in the sector of insects as feed, largely because, until recently, only a few insect species were used to feed livestock, and these insects were not traditionally traded. Nowadays, systems for producing, processing and marketing fly larvae as feed are being developed but gender issues have not yet been really taken into consideration. Efforts should be made to ensure that this emergent activity will also provide business opportunities for women entrepreneurs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".