Exploring the Consent to Touch in the Modern Postural Yoga Class: Perspectives in the Era of #MeToo
Bibliographic record
Abstract
This study explores the issue of touch in yoga classes in the context of #MeToo. Presently, there are no ethical standards which guide yoga teachers in “adjusting” or “enhancing” bodies in a yoga class. The use of touch as a teaching method in Modern Postural Yoga (MPY) classes has increasingly been a topic of discussion in the broader yoga community, particularly through the rise of #MeToo. The purpose of this study was to explore how yoga teachers are approaching touch during yoga classes and if yoga teachers have changed their “hands-on” teaching practices in response to #MeToo. I recruited eleven yoga teachers in Kingston, Ontario and divided these teachers into two focus groups and four individual interviews to explore why yoga teachers touch their students, how yoga teachers approach touching students, and what yoga teachers think about the issue of consent in yoga. Five themes emerged from the data: trust, intention, observation, power, and scope of practice. For the teachers in this study, #MeToo was not a factor in their decision to use or not to use touch during a yoga class. This study suggests that yoga teachers do not follow a consistent approach to receiving consent when offering hands-on assists during a yoga class, but that many yoga teachers would like to challenge existing standards in order to enhance teachers’ qualifications and skills in the areas of safe practice, ethical teaching, and student-centered instruction. The results show that a teacher’s decision to touch is informed by their personal intentions related to teaching, their relationship with students, and their careful observation of students’ bodies during a yoga āsana (shape). While some teachers are questioning the use of touch in relation to social context and social movements like #MeToo, this study suggests that yoga teachers may benefit from evaluating their teaching practices (including touch) in relation to broader public movements like #MeToo.
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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 | 0.028 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.051 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".