Justice, Transaction, Translation: Blackfoot Tipi Transfers and WIPO's Search for the Facts of Traditional Knowledge Exchange
Bibliographic record
Abstract
In this article, I examine the complexities and politics of enrolling one socially embedded form of transaction and knowledge into the terms or practices of another. I look at the correspondences and divergences in how the World Intellectual Property Organization (WIPO) transposed the “facts” of Blackfoot tipi‐transfer practices in efforts to harmonize global intellectual property (IP) regimes and to achieve “justice” and “empowerment.” WIPO's translation work is set against a case where Piikani Blackfoot tipi holders used relational transfer practices to effect a use arrangement, bypassing the means and ends of IP. I argue that looking at WIPO's practices helps us to see anthropology's own epistemological, instrumental, and political constraints, while looking at Piikani transfers helps us to conceive of alternatives. This has bearing across anthropology's disciplinary spectrum where problems of knowledge translation are commonplace.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.079 |
| Scholarly communication | 0.012 | 0.023 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".