Indigenous data sovereignty in intangible cultural heritage governance: A complementary approach to public–private partnerships
Bibliographic record
Abstract
Abstract This article examines the challenges Indigenous communities face in safeguarding their intangible cultural heritage (ICH) in the digital age, using two case studies. Referring to the Te Hiku Media case, it analyzes the threat of data colonialism posed by corporate digitization projects. The article argues that existing legal frameworks provide limited protection for Indigenous ICH, prompting Indigenous communities to develop the innovative theory of Indigenous data sovereignty (ID-SOV). The Government of Nunavut–Microsoft partnership case highlights the benefits and drawbacks of public–private partnerships (PPPs) for Indigenous ICH. Key takeaways from both cases’ analysis lead to our proposal of integrating ID-SOV principles into PPPs to limit data colonialism risks and improve the sustainability of Indigenous ICH digitization projects. The article contends that implementing ID-SOV principles by design and by default in PPPs can empower Indigenous communities while leveraging the oversight of public actors and resources of private partners to safeguard Indigenous ICH through digital tools.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.025 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".