Questions Worth Asking: Un-disciplining Archaeology, Reclaiming Pasts for Better Futures
Bibliographic record
Abstract
Abstract This forum engages an emerging discourse around historical reckoning, truth, and reconciliation, asking how these frameworks inform American archaeology and its future. A growing number of archaeologists are now demanding systemic disciplinary transformations that directly address how white supremacy and settler colonialism enact Indigenous dispossession and erasure as well as anti-Blackness, gender discrimination, and ableism. This forum, featuring 10 archaeologists—including a mixture of junior- and senior-level scholars—is organized into thematic dialogues that highlight their different perspectives and experiences within North American cultural heritage management. First, the dialogue interrogates American archaeology’s embeddedness in ethnocentrism and racism. It then looks at different forms of collaboration that actualize anti-colonial critiques and corrections. Next, it compares collaborative methods with broader calls for “un-disciplining” through incorporating non-Western expertise, sensibilities, needs, and interests. In response to systemic forms of racism, colonialism, and neoliberalism within archaeology, the authors discuss how individuals and institutions can work for and with Indigenous and descendant communities to achieve “reclamation,” defined as the assertion of community control over their significant places, ancestors, belongings, and historical narratives. The article concludes with a consideration of how archaeology can be used by communities to ensure their collective futures.
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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 | 0.036 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.065 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".