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Record W4385876145 · doi:10.5430/wjel.v13n7p387

Understanding Human Emotion: An Intervention of Anger through Raudra Rasa in Dina Mehta’s Drama Brides Are Not for Burning

2023· article· en· W4385876145 on OpenAlexvenueno aff
Kheya Chakravarty, Thenmozhi Mani

Bibliographic record

VenueWorld Journal of English Language · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCultural and Artistic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAngerDramaPsychologyEmbodied cognitionRage (emotion)Anger managementIntervention (counseling)AestheticsSocial psychologyArtVisual artsEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Anger is the main propaganda of this study. This study aims to explore the intervention of anger through the aesthetic concept of Raudra rasa in Dina Mehta's drama, "Brides Are Not for Burning." Emotions play a significant role in human experiences, and anger, in particular, has been the subject of extensive research. Drawing upon traditional Indian aesthetics and performing arts, Raudra rasa represents a complex emotional state of anger, rage, or ferocity. By examining its portrayal and impact in Mehta's drama, this study seeks to enhance our understanding of the role of Raudra rasa in evoking and managing anger. Utilizing a descriptive qualitative method, this study employs how Raudra rasa is embodied and expressed by the character. By delving into the intervention of anger through Raudra rasa in the play, this study contributes to the existing knowledge on the interplay between emotions, art, and human experiences.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.007
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.153
GPT teacher head0.379
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2023
Admission routes1
Has abstractyes

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