Bibliographic record
Abstract
Abstract This essay employs the Gāyatrī mantra as a case study to parse out cultural appropriation’s complex entanglements with modern yoga in North America. Public debates on appropriation in North America are largely based on premises of ownership, authenticity, purity, and corruption; however, theoretical arguments of non-essentialisms implicitly undermine accusations of harmful appropriation. Non-essentialisms potentially decompose fixed cultural essences so that intangible cultural artefacts or cultural groups are not subject to properties of authenticity, purity, and ownership. This position a priori blocks the possibility of appropriation (as conceived in popular discourse) because it deconstructs the premises that frame appropriation. This essay considers non-essentialist arguments, as well as several others that conflict with the possibility of harmful appropriation, such as those based on Indigenous agency and contested identity politics within Hindu-American communities. I argue that even though such arguments are substantially important, they are near sighted if weaponised to wholly dismiss the potential harmful effects of cultural appropriation. They overlook many other problems, such as exclusion, neo-colonial extraction, epistemic violence, and lack of representation of marginalised communities. These further issues are entangled with the Orientalist roots of Spiritual but not Religious ideologies.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.028 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".