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Record W2318087706 · doi:10.1386/safm.3.2.65_1

Consumer citizens and troubling desires: Reading Hindi cinema in Pakistan

2011· article· en· W2318087706 on OpenAlexaff
Shahnaz Khan

Bibliographic record

VenueStudies in South Asian Film & Media · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsHindiMovie theaterNarrativeSociologyAestheticsModernityContext (archaeology)Human sexualityConsumption (sociology)Media studiesAgency (philosophy)Gender studiesPolitical scienceHistorySocial scienceLiteratureArtLaw

Abstract

fetched live from OpenAlex

This discussion draws upon interviews with Pakistani viewers of Hindi cinema and locates them within a context of a declining film industry in Pakistan. I examine the reception of Hindi films among Pakistani audiences, and explore the ways in which their cinematic narratives help infuse western influences with local traditions. In so doing, they also help produce forms of dress, music and lifestyles that have much to say about the fluidity of cultural forms in local contexts in Pakistan, a country that continues to be a land of contrasts. On the one hand, there is the jihadi culture and support for Osama bin Laden, and on the other, a local production of modernity influenced by consumerism, redefined sexuality and Shah Rukh Khan. I argue that Hindi films target potential consumers and create desires that trouble as much as they entice. Through a montage of cinematic narrative, cosmopolitan lifestyles, dazzling wardrobes and distilled glamour, Pakistanis participate in consumption patterns emerging in the country. At the same time, sexuality is in a state of flux. While conservative forces are trying to regulate their mobility and sexuality, young people are challenging traditions and received norms. Such a process renders Hindi cinema an important yet, to some viewers, troubling component of public culture in Pakistan. The emerging public culture identifies complex and dynamic processes that allow for new types of possibilities and arrangements as the lives and agency of individuals interweave with shared cinematic texts and the social structures within which they are played out.

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)0.002
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 score0.017
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.008
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0020.004
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 head0.109
GPT teacher head0.305
Teacher spread0.196 · 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
Published2011
Admission routes1
Has abstractyes

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