Intersecting Aesthetics of Suffering: Emotional Expression and Clinical Realism in “The Stories of Shanti” and “The Steel Windpipe”
Bibliographic record
Abstract
This article builds on the complexity of suffering as manifest in the two texts, “The Stories of Shanti: Culture and Karma” and Mikhail Bulgakov's “The Steel Windpipe” that foreground the interaction of emotional expression, culture, clinical realities, and ethical concerns. It explores the conflict of traditional healing paradigms in relation to contemporary medical practice. A comparative textual analysis approach to illustrate how suffering is constructed, defined, and experienced across different cultural worlds and how, by extension, medical practitioners navigate ethical duties amid cultural safety concerns regarding biomedical requirements has been used. Bringing together the religious readings of suffering, personal testimony, and elements of medical realism, it argues that suffering is not only an individual pathology but is relational, culturally mediated, and spiritually interpretive. The representations of Shanti and Lidka illustrate how meaning-making, emotional resilience, generosity, familial involvement, and medical ethics (both patient-centered and in relation to their caregivers), can shape the lived experience of illness. The concluding remark of this paper is that suffering is not just a physical state but also rather an integrated state of mind, body, and spirit, influenced by different cultural perspectives.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".