Indigenous Governance of Cultural Heritage: Searching for Alternatives to Co-Management
Bibliographic record
Abstract
In this paper, we critically examine the co-management of Indigenous peoples’ cultural heritage as simultaneously a driver and product of the culturalisation of Indigenous peoples: the reduction of complex legal- political orders, anchored in specific lands, value systems, rights, and prac- tices, to material cultures. Co-management has been hailed as a defensibly imperfect, ‘tweakable’ system that benefits both Indigenous and state parties, and moreover, a stepping stone to Indigenous self-determination. Departing from these analyses, we argue that co-management is not just an administrative arrangement but also a state-ratified international rights regime, and accordingly, that it cannot do other than undermine Indigenous self-determination and imperil Indigenous peoples’ cultural heritage. We suggest that cultural heritage can only thrive by being actively engaged with in situ: via the living practice of Indigenous governance. Operationalising our argument, we first consider the challenges of cultural heritage protection in Sapmi; specifically, the co-management of Laponia, in Sweden, and the unprotected sacred area of Suttesaja in Finland. We then discuss a more promising framework: the Quechua ‘Biocultural Heritage Territory’ of the Parque de la Papa, in Peru. Finally, we apply the lessons of the Parque to Suttesaja, showing how this opens up governance-based avenues to safeguarding Indigenous sacred areas.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.050 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".