What makes us squirm - a critical assessment of community-oriented archaeology
Bibliographic record
Abstract
We provide a critical response to Andrew Martindale and Natasha Lyons’ 2014 special section on Community-Oriented Archaeology (Canadian Journal of Archaeology Volume 38, Issue 2), discussing the authors’ definitions, interpretations, and motivations around archaeology and community. By not defining archaeology in terms of how it is most commonly practiced, we argue the collective work misses the mark, with serious consequences for descendent communities. We show how Community-Oriented Archaeology appropriates the challenge posed to archaeologists to make their discipline relevant and responsive to Indigenous communities; instead, the authors foreground archaeology itself and reaffirm the privilege of non-Indigenous archaeologists, especially academic archaeologists. By considering what is excluded and taken-for-granted, we examine the special section in terms of selection bias and revisionist history. We suggest Community-Oriented Archaeology co-opts aspects of Indigenous, critical, and radical discourses to legitimize the institution and practice, in the process forgetting what is at stake for Indigenous peoples. Rather than focusing on the needs of archaeology and archaeologists, we emphasize the interests of Indigenous communities and address uncomfortable truths about institutional racism and systemic inequality. As the editors had hoped, Community-Oriented Archaeology makes us “squirm,” but not for the reasons they intended.
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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 | 0.137 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.040 | 0.137 |
| Scholarly communication | 0.046 | 0.046 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.013 | 0.018 |
| 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".