Posthuman Potentials: Considering Collaborative Indigenous Archaeology
Bibliographic record
Abstract
This essay argues for the diversity and promise of posthuman approaches in archaeology by dispelling blanket critiques, by differentiating between distinct lines of post-anthropocentric thought and by pointing to parallels between Posthumanism and collaborative Indigenous archaeologies. It begins by arguing that symmetrical archaeology is but one part of the diverse body of thought labelled ‘posthuman’. Next, it explores broader posthuman engagements with political issues relevant for collaborative Indigenous archaeologies, particularly concerns regarding under-represented groups in the field. Finally, it identifies flat ontologies as key components of posthuman approaches, clarifying what this term means for different lines of post-anthropocentric thought and briefly considering how the concept of flatness compares with Indigenous metaphysics.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.053 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 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".