Labelling and certification schemes for Indigenous Peoples' foods
Bibliographic record
Abstract
This review, for the first time to date, analyses the potential of labelling and certification schemes for Indigenous Peoples to market their food products. Specifically, it looks at those schemes that are designed by, with and for Indigenous Peoples, and that can provide economic, social and environmental benefits while protecting and promoting their unique values centered around the respect of life and Mother Earth. Eleven examples in this review cover innovative schemes implemented by Indigenous Peoples and practitioners in Africa, Asia, Central and South America and Oceania. They include territorial labels, geographical indications (GI), and participatory guarantee schemes (PGS), among others. In addition, the publication features one case study of a community-supported agriculture (CSA), as alternative example to engage with Indigenous Peoples and reaching out the market. Important factors that lead to the success of different schemes include (1) the leadership and ownership of Indigenous Peoples in the initiative (2) adequate support by external stakeholders including public and private sector, and universities (3) raising consumer awareness and education on Indigenous food products via fairs, festivals and other platforms, and (4) designing value chains and policies in a way that harmonize local, domestic and international trade. The review includes recommendations for various actors to support Indigenous Peoples in their self-determined economic development and towards the sustainable marketization of their products. The review also provides guidelines for Indigenous Peoples willing to engage in such initiatve. Those are applicable to different contexts on the ground, and include good practices, and measures to mitigate risks.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".