Embodying Transnational Yoga: Eating, Singing, and Breathing in Transformation, by Christopher Jain Miller
Bibliographic record
Abstract
In Embodying Transnational Yoga: Eating, Singing, and Breathing in Transformation, Christopher Jain Miller examines the significance of food, music, and breathing practices within transnational yoga communities.To explore these themes, which Miller suggests are understudied within Yoga Studies, the book offers an ethnographic account of three communities: Gurani Anjali's Yoga Anand Ashram in Long Island, New York; Polestar Gardens, located on the Big Island of Hawaii, which follows the teachings of Paramahansa Yogananda; and Swami Kuvalayananda's Kaivalyadhama Yoga Institute in Lonavala, Maharashtra.The book uses the phrase "transnational yoga communities" to emphasize the interconnected nature of the practice, showcasing the convergence of various ideas and practices in diverse global settings.By examining these communities, Miller illustrates that, in line with the prevailing cliché in popular yoga culture, "yoga is more than just the postures" (1).Throughout the book, Miller emphasizes the importance of taking an approach that is simultaneously critical and sympathetic, and encourages future scholars to embrace a similar perspective.The text acknowledges potential objections that may arise to the sympathetic approach, especially given the increasing focus on guru abuse and legal allegations within contemporary yoga communities.1
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".