Life in a Body: Counter Hegemonic Understandings of Violence, Oppression, Healing and Embodiment among Young South Asian Women
Bibliographic record
Abstract
This study is an investigation of embodiment. It is informed by the experiences and understandings of health, healing, violence and oppression among 15 young South Asian women living in Toronto, Canada. Their articulation of the importance of, and difficulties associated with, health and healing in contexts of social inequity contribute to understandings of embodiment as co-constituted by sentient and social experience. In my reading of their contributions, embodied learning – that is, an ongoing attunement to sentient-social embodiment – is a counter hegemonic healing strategy that they use. Their experiences and insights support the increasingly accepted claim that social inequity is a primary determinant of health that disproportionately disadvantages subordinated people. Furthermore, participants affirm that recovery and resistance to violence and oppression and its consequences must address sentient-social components of embodiment simultaneously. In this study, Yoga teachings provide a framework and practice to investigate embodiment and embodied learning. Following 12 Yoga workshops addressing health, healing, violence and oppression, I conducted individual interviews with 15 workshop participants, 3 Yoga teachers and 2 counsellor / social workers. Participants discuss Yoga as a resource for addressing mental, physical, emotional and spiritual consequences of violence and oppression. They resist New Age interpretations of Yoga in terms of individualism and cultural appropriation; they also challenge both New Age and Western biomedicine for a lack of attention to the consequences of social inequity for health and healing. This study considers embodied learning as an important healing resource and form of resistance to violence and oppression. Scholarship addressing embodiment in sociology, health research, anti-racism, feminism, anti-colonialism, decolonization and Indigenous knowledges are drawn upon to contextualize the interviews. This study offers insights relevant to health promotion and adult education discourse and policy through a careful consideration of the embodied strategies used by the participants in their nuanced negotiations of social inequity and pursuits of health and healing.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".