The cultural right to practice traditional medicinal knowledge in Zimbabwe /
Bibliographic record
Abstract
Cultural resources like traditional medicinal knowledge need to be recognized in their role tied to important regional practices in Zimbabwe. This is especially as a nexus of legal definitions for biological, intellectual and cultural resources will inform a National Sui Generis Legislation for the protection of these resources. Even further, because foreign pharmaceutical companies seeking plant genetic resources, called 'green-gold', benefit from derivatives of traditional medicinal knowledge it is important to protect these practices as part of an entire social and symbolic system. This system can be conceived as traditional medicinal knowledge is accessed, kept, shared, used and valued as a spiritual gift that links individuals, families and community in relationships. Change to this arrangement occurs when the derivatives of it are appropriated for local non-customary use in Zimbabwe's street markets, in a trade union of traditional-healers, as well as for research and development schemes. Acknowledging the spectrum of divergent interests and practices surrounding traditional medicinal knowledge is a prerequisite to creating a system of protections for it as a cultural resource. A National Sui Generis Legislation framework that clearly supports and protects the cultural right of local individuals and communities will thereby need to identify the important customary and non-customary regional practices around traditional medicinal knowledge and create entitlements to them accordingly.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".