Document Indigenous Food Ingredients in China through Youth Participation
Bibliographic record
Abstract
Abstract Problem Indigenous food systems can affect multiple aspects of Indigenous people’s health and contribute to more efficient, sustainable, resilient, and equitable food systems. However, although tens of Indigenous groups live in China, very few projects have targeted their traditional or Indigenous food systems and ingredients. Description of the problem The main purposes include documenting traditional and Indigenous foods and medicine, building multiple social media for public impact, encouraging Indigenous youth participation, and advocating a more sustainable food system in China Results With the collaboration of Good Food Fund China, the project recruited 257 volunteers from 27 ethnic groups. The project collected 418 traditional food ingredients, produced 18 TikTok short videos to introduce Indigenous ingredients with the maximum number of views in a single post of more than 100,000, organized 16 Indigenous food culture knowledge webinars, translated 14 international case studies in Indigenous food systems with the permission of authors, and published 16 articles about Indigenous food ingredients and culture in the writing camp. The online lives also received more than a million total views. The results were presented at COP26 and COP28. More results will continue to be presented to the public through articles, books, academic papers, documentaries, short videos, online webinars, and other forms. Lessons The project demonstrates the potential to use online platforms to advocate and document sustainable food systems and biodiversity through youth participation. More similar projects can be designed in different parts of the world to support sustainable food systems transformation and ignite down-to-earth changes. However, funding and publication channels are still difficulties in the project. Key messages • The first national level youth participation project to document Indigenous food biodiversity in China. • Take advantage of social media and the internet for public impact.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".