Angel Mounds Repatriation: Archaeology of a Final Resting Place
Bibliographic record
Abstract
Abstract With a legal framework in place for decades, NAGPRA repatriations are becoming more common. Yet because of myriad factors, progress can be slow. No universal repatriation models exist for curatorial institutions and Tribes to employ because each repatriation, descendant community, and stakeholder constituency is unique. In this article, the processes associated with the archaeological preparations and reinterment procedures for a major reburial of repatriated remains at the Angel Mounds archaeological site are described. Navigating the needs, goals, and wishes of a diverse group of partners and other stakeholders is discussed. The article begins with a brief overview of the NAGPRA-related literature pertinent to the issues we faced. Because Angel Mounds is a state historic site, a National Register of Historic Places property, and a National Historic Landmark, we were required to follow both state and federal guidelines for breaking ground at the site. It was important to everyone that the new grave not disturb extant archaeological resources or other burial features and be designed to facilitate reinterments of any NAGPRA items discovered in the future. Careful collaborative planning was required in order to identify a grave location, while the archaeological study was intended to minimize the impact of the reburial on in situ cultural resources.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".