Bodily Contraction Arises with Dukkha: Embodied Learning to Foster Racial Healing
Bibliographic record
Abstract
Black somatic therapist Resmaa Menakem has persuasively argued that racism exist in our bodies more than our heads and that racial healing requires learning to become mindful of our embodied states. The reason that racism remains prevalent despite decades of anti-racist education and the work of diversity and inclusion programs, according to Menakem, is that racist reactions that shun, harm, and kill black bodies are programmed into white, black, and police bodies. The first step in racial healing, from this point of view, is to shift the focus from cognitive solutions to an embodied solution, namely, embodied composure in the face of stressful situations that enables everyone to act more skillfully. Similar to how racial healing has been hampered by a misguided overemphasis on cognitive interventions, might our teaching be analogously encumbered by lack of attention to the bodies of teacher and students? In this article, I emphasize the value of cultivating body awareness in the classroom. I introduce an embodied exercise that teaches students to recognize embodied clues of the experience of dukkha, the first āryasatya. Through such exercises, students take a step towards acting more skillfully and intentionally in stressful situations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".