Indigenous Knowledges and Knowledge Codification in the Knowledge Economy
Bibliographic record
Abstract
The production, dissemination and archivization of knowledge are important processes in contemporary knowledge economy. Questions on Intellectual Property Rights (IPR) arise when Indigenous Knowledges (IKs) are examined and evaluated on how they benefit the knowledge economy and development. These questions seem to be addressed in terms of dominant epistemological ideologies based on Eurocentric knowledge production philosophies embedded in positivism and how knowledge is codified and patented. The purpose of this chapter is to examine the process and effect of codification and on IKs. The chapter argues that while knowledge codification is necessary for IKs to be preserved and archived, it is important not to lose sight of the communal ownership of the knowledges and to protect them from exploitation and appropriation. The chapter concludes that while codification of IKs and intellectual property rights are controversial, for IKs to play their full role in socioeconomic development they cannot be left out of codification that is pervasive in today's knowledge economy and society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".