Taming the Ontological Wolves: Learning from Iroquoian Effigy Objects
Bibliographic record
Abstract
ABSTRACT Currently on the rise in archaeology, ontological approaches promise new ways of engaging with alterity of various kinds—different people, different times, different forms, even different worlds. This work promises to aid in critical reflections on the arbitrary nature of the Western gaze and to recognize and incorporate non‐Western knowledge in new manners. There are, however, several challenges to address. First, as noted by several leading thinkers in this area, the present range of ontological approaches include contrasting theoretical underpinnings. Second, these approaches are rarely considered in relation to the practical challenges of specific archaeological cases, particularly contexts of settler colonialism in which practitioners are attuned to the potential colonial nature of their work. I divide ontologically engaged archaeologies into three related but distinct groups and use a small museum assemblage of seventeenth‐century Wendat materials from Ontario to help think through these three theories. In comparing approaches, I outline their respective strengths, weaknesses, and points in need of further clarification. I conclude that the ontological turns offer new and valuable angles of articulation with archaeological materials but that archaeologists must adopt them cautiously if they are to avoid repeating or continuing some of the darkest parts of our (colonial) disciplinary history. [ontology, archaeology, new materialism, archaeological theory, effigies, colonialism, Iroquoian archaeology, Ontario]
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".