Crossing the Border: Indigenous Solidarity and Sovereignty within International Repatriations
Bibliographic record
Abstract
The relationships between museums and the Indigenous Peoples of Turtle Island have changed monumentally over the last 20 years. Repatriation has gone from a contested topic to a reality of museum–Indigenous relationships. Despite the legitimization of repatriation, there still exist numerous obstacles for Indigenous people seeking the return of their sacred and cultural objects. One unique challenge is that of international repatriation. The international borders that we take for granted today arose out of settler politics and have no basis within Indigenous history. The United States (US)–Canada border inadvertently separated and split numerous Indigenous nations, significantly contributing to cultural fracturing and weakening. Museum collections of Indigenous material culture are nationally isolated, despite containing large collections from Indigenous groups outside their borders. This necessitates an original approach to repatriation that is not covered in national policy or legislation. International repatriation requires a high level of cooperation between Indigenous groups, giving nations split by the border a chance to reconnect and form a united front in order to achieve their objectives. Although the imposition of nation–state borders created many barriers for Indigenous Peoples, cross–border repatriation offers unique opportunities to assert Indigenous solidarity, sovereignty, and healing.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".