Moving in time, out of step: mimesis as moral breakdown in European re‐enactments of the North American Indian Woodland
Bibliographic record
Abstract
What constitutes a dance step executed just right? Does its success reside in its faithfulness to an ‘original’ model or script or in a feeling experienced by the dancer interpreting the model in a new context? This is the kind of epistemological, and moral, dilemma that was often voiced during my fieldwork amongst Indianists, amateurs involved in re‐enactment of Native American lifeworlds on European soil. In Indianism, museum‐quality replicas made by and worn on European bodies function as heuristic tools in exploring ‘what life was really like’ in other times and places. Focusing on Woodland Indianist performances and replicas in a variety of European settings, I suggest that Indianism, as an amateur engagement with re‐imaginings and reifications of the North American Indian, faces constant moral breakdown because of its unease with the transformative nature of mimesis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".