Green Health in Guatemala: How can we build mutual trust and partnerships to develop an evidence-base for local medicines and realize their potential?
Bibliographic record
Abstract
The implementation of access and benefit-sharing (ABS) protocols, and especially the Nagoya Protocol, has created new hurdles for international collaborations around Indigenous Traditional Knowledge. Overall, these frameworks push for the development of novel collaborative North–South agendas to improve the fair distribution of benefits. The Green Health Project (Guatemala) aims to implement a culturally pertinent and mutually accepted framework for sustainable use, as well as ABS of traditional medicinal plants. It involves developing a consensus among Indigenous groups, government officials, industry, and academia. We describe steps undertaken to design and implement an intercultural transdisciplinary process that promotes trust building and advances herbal medicine research in a respectful and innovative way. This involves joint definition of goals and methods. The consortium co-researched Q’eqchi’ Maya traditional medicine, collected voucher specimens of medicinal plants with traditional Healers, identified their taxa, and later developed a literature-based evaluation identifying species for potential product development. No samples for further research and development were collected. By applying the emic–etic concept, the project helped improve understanding of the main drivers of each stakeholder and the associated obstacles for reaching an ABS agreement. The project also explored the emergence of potential new drivers for developing evidence-based herbal medicine from the perspectives of academia, policy, cooperation, and grassroots Indigenous movements.
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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 | 0.024 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".