Protection of Traditional Knowledge: The Work and the Role of International Organisations and Conferences
Bibliographic record
Abstract
The concepts of traditional knowledge, indigenous people and indigenous knowledge have gained broad use in international discussions on sustainable improvement. Nevertheless, their use is usually subjected to confusion. There have been numerous attempts to clarify the notions of traditional knowledge, indigenous knowledge and people. Although there are no globally approved definitions, this paper is devoted to analysing the approaches of international organisations to the protection of traditional knowledge. The study discusses the work of the international organisations: WIPO, UNESCO, WHO, WTO and FAO, as well as international conferences: Conference of the Parties of Convention on Biological Diversity and UNCTAD. The examples of traditional knowledge illustrate the paper: use of turmeric for wound healing in India, use the Hoodia plant to suppress hunger during hunting in South Africa and others. It is noted that today, at the international level, there are no instruments that ensure comprehensive protection of traditional knowledge. The author concludes that WIPO and UNESCO currently carry out the main work aimed at providing the protection of traditional knowledge. However, other international organisations and conferences, such as WHO, WTO, FAO, UNCTAD and Conference of the Parties of CBD in their work are also addressed some aspects associated with the preservation of traditional knowledge.
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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 | 0.035 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.031 | 0.022 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".