EXPLORING THE EXPERIENCE OF AWE THROUGH THE LENS OF YOGIC PRACTICE: AN INTERPRETATIVE STUDY BASED ON THE PĀTAÑJALA YOGA SŪTRA
Bibliographic record
Abstract
Awe is a widely discussed topic in contemporary research, recognized as a positive emotion due to its diverse benefits across various domains of life. Western psychologists suggest that the experience of awe can play a vital role in mitigating stress in today’s fast-paced world, helping to balance and moderate its effects. In their paper Awe and the Interconnected Self, Susan K. Chen and Mariam Mongrain (Department of Psychology, York University, Ontario, Canada) argue that this positively valanced emotion can be cultivated, and that an individual's capacity for absorption may be a key antecedent in experiencing awe. Absorption refers to one’s ability to fully engage with the world by utilizing perceptual, motoric, imaginative, and cognitive resources (Tellegen & Atkinson, 1974, p. 274). In yogic philosophy, kaivalya (liberation) is achieved through the practice of Ashtanga Yoga (the eight-limbed path). While kaivalya and the emotion of awe are fundamentally different concepts, the practices outlined in Ashtanga Yoga may foster conditions that encourage the experience of positive awe. I propose that elements of yoga, particularly Yama, Niyama, and Asana, can help regulate and enhance the experience of awe. This paper explores the potential for yoga to regulate awe and investigates the connection between yogic practices and awe, with the aim of demonstrating how this relationship can contribute to a more positive and meaningful life.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".