Integrating Remote Sensing and Indigenous Archaeology to Locate Unmarked Graves
Bibliographic record
Abstract
Abstract Archaeologists have long been called on to use geophysical techniques to locate unmarked graves in both archaeological and forensic contexts. Although these techniques—primarily ground-penetrating radar (GPR)—have demonstrated efficacy in this application, there are fewer examples of studies driven by Indigenous community needs. In North America, the location of ancestors and burial grounds is a priority for most Indigenous communities. We argue that when these Indigenous voices are equitably included in research design, the practice of remote sensing changes and more meaningful collaborations ensue. Drawing on Indigenous archaeology and heart-centered practices, we argue that remote-sensing survey methodologies, and the subsequent narratives produced, need to change. These approaches change both researchers’ and Indigenous communities’ relationships to the work and allow for the inclusion of Indigenous Knowledge (IK) in interpretation. In this article, we discuss this underexplored research trajectory, explain how it relates to modern GPR surveys for unmarked graves, and present the results from a survey conducted at the request of the Chipewyan Prairie First Nation. Although local in nature, we discuss potential benefits and challenges of Indigenous remote sensing collaborations, and we engage larger conversations happening in Indigenous communities around the ways these methods can contribute to reconciliation and decolonization.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".