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Record W2530635335 · doi:10.5195/cinej.2016.138

Bollywood of India: Geopolitical Texts of Belonging and Difference and Narratives of Mistrust and Suspicion

2016· article· en· W2530635335 on OpenAlexaff
Iqbal Shailo

Bibliographic record

VenueCINEJ Cinema Journal · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsCARE Canada
Fundersnot available
KeywordsGeopoliticsNarrativePartition (number theory)Identity (music)Construct (python library)Meaning (existential)Gender studiesHistorySociologyPolitical scienceAestheticsLiteratureArtLawPsychology

Abstract

fetched live from OpenAlex

A number of Bollywood films create meaning and geopolitical narratives through dialogue, raw images, settings, costumes and historical contexts. This study examines three contemporary Indian films —Earth (Deepa Mehta, 1998), Lagaan (Ashutosh Gowariker, 2001) and Sarfarosh (John Matthew Matthan, 1999) — that explore sub-continental history with a particular focus on insecurity, mistrust and suspicion. It discusses how socio-cultural and regional differences are (re)produced and how geopolitical meanings of ‘we’ and ‘they’ are narrated and constructed through Bollywood. These films construct an image of identity, belonging and difference, emphasizing that Hindus and Muslims are Indians however some legacies and suffering brought on during the partition of British India are still alive in memories when discrimination and exclusion are practiced in their ancestral homelands.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0110.012
Scholarly communication0.0090.004
Open science0.0010.004
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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 designQualitative
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
Published2016
Admission routes1
Has abstractyes

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