A COMPARATIVE ANALYSIS OF REPATRIATION OF NATIVE AMERICAN ARTIFACTS AND HUMAN REMAINS LAWS IN MONTANA, USA AND ALBERTA CANADA
Bibliographic record
Abstract
ABSTRACT: Native American and Indigenous communities across the United States and Canada have lost an extensive amount of human remains and sacred artifacts to non-Native people exhuming Native American and Indigenous burial sites that may have been dug up for personal gain, stolen, placed in museums, or left in the hands of non-Native collectors. The repatriation of human remains and sacred artifacts to Native nations can be a lengthy, political, and challenging process yet it is worth the effort for Native people. Native American advocacy and evolving public sentiment toward Native people have led to legislative advancements in the United States and Canada that have made it somewhat easier in certain circumstances to return the skeletal remains of loved ones and invaluable items of cultural patrimony to the original owners. Today, in the United States there is a repatriation law in place to help Native people in this predicament. Unfortunately, there is no across-the-board repatriation laws or legal process in place right now in Canada for Indigenous people. This thesis explains why repatriation laws are urgently needed and provides a review and comparison of the legal process and repatriation laws in the US and Canada. My research concludes that Indigenous Canadians could benefit by working with the Canadian government to adopt and implement repatriation laws similar to those already in place in the US.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".