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Record W4382991752 · doi:10.1386/ffc_00053_1

Bollywood self-fashioning: Indian popular culture and representations of girlhood in 1970s Indian cinema

2023· article· en· W4382991752 on OpenAlexaff
Sony Jalarajan Raj, Adith K. Suresh

Bibliographic record

VenueFilm Fashion & Consumption · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsMacEwan University
Fundersnot available
KeywordsMovie theaterPopular cultureGender studiesAestheticsSociologyStyle (visual arts)Power (physics)Psychology of selfMedia studiesArtPsychologyLiteratureSocial psychology

Abstract

fetched live from OpenAlex

This article investigates how Bollywood cinema represented girlhood experiences in India in the early 1970s. It argues that the films during this time focused on representing girls who displayed a variety of new fashion styles and attitudes, some of which were borrowed from western cultures. This was a sign that there was a new way of representing girls which broke with the submissive, dull and melancholic sari-wearing Indian female stereotype entrapped within domestic settings. The immediate result of this was the emergence of new style leaders and popular icons in Indian popular cinema. This study uses Stephen Greenblatt’s concept of self-fashioning and Guy Mankowski’s idea of self-design to examine how Indian girlhood was renegotiated in the 1970s as an individual-centric idea with more agency and power. Here, self-fashioning refers to the way girls adopt new elements of fashion, styles and attitudes to distinguish their identity from earlier archetypal modes of representation in film and culture. It specifically analyses the emergence of Jaya Bhaduri in Guddi (1971) and Dimple Kapadia in Bobby (1973) as case studies to understand the transformation of girlhood representations in early 1970s Bollywood that opened a new space for girls to redefine their selfhood through the assimilation of consumerism, western culture and fashion styles.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score0.728

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 studies0.0000.000
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 head0.026
GPT teacher head0.259
Teacher spread0.233 · 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 designObservational
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

Citations3
Published2023
Admission routes1
Has abstractyes

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