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Record W4408455764 · doi:10.51767/ic250309

EXPLORING THE EXPERIENCE OF AWE THROUGH THE LENS OF YOGIC PRACTICE: AN INTERPRETATIVE STUDY BASED ON THE PĀTAÑJALA YOGA SŪTRA

2025· article· en· W4408455764 on OpenAlexaboutno aff
V Unnimaya

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicIndian and Buddhist Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.526
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.199
GPT teacher head0.336
Teacher spread0.137 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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