Book Review: Law, Knowledge, Culture: The Production of Indigenous Knowledge in IP Law
Bibliographic record
Abstract
Jane E. Anderson examines how diverse factors have shaped the negotiation of Indigenous knowledge (IK), especially in the evolution of Australian copyright. The emergence of claims to protect IK in an intellectual property framework are contextualized in the colonialist experience of exploitation. Part 1 gives a history of IP, showing that it is not a neutral area of law. IP related to IK is marked by the power relationship between Indigenous and settler peoples. The IP framework pigeonholes IK in an effort to maintain a facade of objectivity in a clearly subjective area of law. Part 2 discusses the politics of law, and the role of IP law in the commoditization of Aboriginal art/IK and co-optation of the cultural aspects of that art. Chapter six discusses case law regarding IK in IP, and underscores the judicial conservatism in challenging the rigid categories of IP law. Part three discusses culture as the only factor that differentiates IK within IP law. The author notes that appealing to culture in IP falsely assumes homogeneity of Indigenous peoples and perpetuates colonial bias and hegemony. The author gives a brief exploration of international instruments protecting IK, but fails to discuss the importance of the Convention on Biological Diversity for IK. The author concludes with a critique of the Australian national approach to IK. This is book is an interesting addition to the body of scholarship at the interface of IK and IP law. It is well-researched and well-written and would be resourceful for scholars from diverse disciplinary backgrounds.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.015 |
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".