Reconceptualizing Repatriation as the Power to Decide
Bibliographic record
Abstract
ABSTRACT Repatriation in Canada is highly situational, with relatively few regulations in place to assist Indigenous communities in returning their cultural belongings home, let alone to protect their rights as stewards. This paper provides a public policy analysis of repatriation regulations in Canada, with special attention paid to the situation they present to the public, to recommend areas for change. We juxtapose these findings with Indigenous repatriation policies and an ethnographic case study partnered with the Tłı̨chǫ Government, which reinterprets repatriation using Tłı̨chǫ traditions for caring for cultural heritage. We focused on two areas of inquiry concerning (1) the identification of existing conflicts between western and Indigenous repatriation directives (e.g., laws, policies, and regulations); and (2) the exploration of repatriation as imagined through an Indigenous lens. Our findings show that we must reconceptualize repatriation from an exchange between two parties into the reclamation of the power to decide how heritage is managed by descendant communities. Contrasting policy analysis with ethnographic data, we found that such a reorientation can only be achieved through both practice and law reform aimed not at strict regulation, but at providing a secure basis upon which Indigenous communities can resituate themselves as recognized stewards of their cultural heritage at home and afar.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.074 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".