Reflections on a Decade of Practice in Indigenous Archaeologies: A Perspective from Canada
Bibliographic record
Abstract
My first attempt at applying a perspective grounded in Indigenous archaeology to the study of cultural landscapes arose from my dissertation research in 2014 (Amundsen-Meyer 2014a). I attempted to braid Indigenous and Western knowledge (i.e., Wall Kimmerer 2013) to more fully understand the past, which I firmly believed was the way forward to decolonize the discipline of archaeology. Since 2019, I have had the privilege of working with, for and on the Siksika First Nation and of learning from many Elders and Knowledge Holders in the community. Reflecting on these experiences, I have begun to wonder if braiding knowledge may, in some cases, contribute to ongoing colonialism in archaeology. This paper will explore the concept of Indigenous archaeology and pose questions and put forward suggestions for future collaborative work as we come to terms with the longstanding colonial history of our discipline.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".