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Record W2428090974 · doi:10.1177/0973258616644814

Dissent and Displacement of Subalternity in Malayalam Cinema: A Cultural Analysis of <i>Papilio Buddha</i> by Jayan K. Cherian

2016· article· en· W2428090974 on OpenAlexaff
Sony Jalarajan Raj, Swapna Gopinath, Rohini Sreekumar

Bibliographic record

VenueJournal of Creative Communications · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsMacEwan University
Fundersnot available
KeywordsGautama BuddhaSubalternMovie theaterTheme (computing)MalayalamSociologyState (computer science)Film industryNarrativeHEROHistoryAestheticsIdentity (music)Gender studiesLiteratureMedia studiesBuddhismLawArtPolitical sciencePhilosophyLinguistics

Abstract

fetched live from OpenAlex

The theme of subalternity with its inherent ramifications is yet to find favour among film makers in India. Progressive film makers of the 1960s attempted to address the theme of subaltern and dared to give the subaltern a voice, but they remained singular attempts. Through a case study on a Malayalam film (a regional film industry from the state of Kerala in India) Papilio Buddha this article tries to analyze the representation of Dalit community in Indian cinema. Though Malayalam film industry has tried to address the concern of Dalits, they have been stereotyped in many ways and reduced to being sidekicks to villains or unskilled labourers having no identity. They remained as instruments to idolize the hero, to act as a contrast to the elite protagonist or as the poor helpless victims who offer the protagonist an opportunity to display his heroism. Papilio Buddha grabbed media attention when it was denied clearance by the censor board as it explores the territory of Dalit consciousness by focusing the lens on the land strike by the Dalit communities and creating a counter narrative to the hitherto idealized images created by the state.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.578

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 head0.027
GPT teacher head0.285
Teacher spread0.258 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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