Bibliographic record
Abstract
As a number of global legal and political institutions grapple with ways to recognize and integrate TMK into their institutional frameworks, how traditional practices are 'recognized', and what work 'recognition's being asked to do become key questions. Three international frameworks that play a key role in recognizing TMK in the international arena are the Convention on Biological Diversity, the World Intellectual Property Organization and the World Health Organization. By examining the way in which these three bodies have recognized and integrated TMK into their respective regimes, while and drawing on the scholarship of anthropologists, critical legal scholars, intellectual property experts and legal and policy literature, I will argue that the recognition of TMK in the international legal and political arena has led to the creation of complex legal and political spaces where recognizing traditional medicinal knowledge has fragmented it, siphoning off the social, cultural and spiritual aspects of it that remain incompatible with the current neoliberal paradigms. Simultaneously, recognition and integration have been used to co-opt traditional knowledge in order to extend governance regimes that integrate TMK and its holders without challenging the basic, outdated and highly unequal and unethical power relations on discourses of recognition are based.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.075 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".