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Centering Women's Bodies: The Significance of Female Representation in Mehta's Element Trilogy

2025· article· en· W4408916240 on OpenAlexaboutno aff
Ajay B. Lawange -, Pradnya D. Deshmukh -

Bibliographic record

VenueInternational Journal For Multidisciplinary Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsTrilogyElement (criminal law)Representation (politics)Gender studiesArtAestheticsSociologyLiteraturePolitical scienceLaw

Abstract

fetched live from OpenAlex

ABSTRACT- Gender and sexuality are central to the pleasure principle in Deepa Mehta’s films. As an Indo-Canadian filmmaker, Mehta is best known for her Element Trilogy, which includes Fire (1996), Earth (1998), and Water (2005). These films critically explore the condition of women in India, focusing on their oppression and marginalization by patriarchal systems. In Fire, Mehta portrays the unfulfilled marriages of Seeta and Radha, who seek emotional and sexual freedom through a relationship with each other. Earth, set during the 1947 partition, examines the devastating impact of political violence on women, narrated by a young Parsi girl named Lenny. Water depicts the lives of widows in 1938 India, showcasing their suffering and societal rejection. Through these films, Mehta highlights the struggles of women and places their experiences at the center of the narrative, emphasizing the intersection of gender, sexuality, and social oppression in India.

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: none
Teacher disagreement score0.012
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.014
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0060.000

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 head0.125
GPT teacher head0.501
Teacher spread0.377 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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