Thematic Analysis of Indigenous Perspectives on Archaeology and Cultural Resource Management Industries
Bibliographic record
Abstract
Abstract This article explores Indigenous perspectives on archaeology in Canada and the United States and the role of archaeologists in engaging with Indigenous communities. As part of our study, we interviewed Indigenous community members about their experiences in archaeology and their thoughts on the discipline. We analyzed each interview thematically to identify patterns of meaning across the dataset and to develop common themes in the interview transcripts. Based on the results of our analysis, we identified six themes in the data: (1) Euro-colonialism damaged and interrupted Indigenous history, and archaeology offers Indigenous community members an opportunity to reconnect with their past; (2) archaeological practices restrict access of Indigenous community members to archaeological information and archaeological materials; (3) cultural resource management (CRM) is outpacing the capacity of Indigenous communities to engage meaningfully with archaeologists; (4) the codification of archaeology through standards, guidelines, and technical report writing limits the goals of the discipline; (5) archaeological methods are inconsistent and based on individual, or company-wide, funding and decision-making; and (6) archaeological software offers a new opportunity for Indigenous communities and archaeologists to collaborate on projects.
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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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".