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Record W2038714095 · doi:10.1075/prag.14.2-3.12sha

Reel to real

2015· article· en· W2038714095 on OpenAlexfundno aff
Shalini Shankar

Bibliographic record

VenuePragmatics Quarterly Publication of the International Pragmatics Association (IPrA) · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicSouth Asian Cinema and Culture
Canadian institutionsnot available
FundersYork UniversitySpencer Foundation
KeywordsHindiIndexicalityIdentity (music)SociologyNarrativeHeteroglossiaStyle (visual arts)EthnographyLinguisticsMedia studiesHistoryAnthropologyLiteratureAestheticsArt

Abstract

fetched live from OpenAlex

Diasporic media, though widely discussed theoretically and occasionally ethnographically, are seldom explored with explicit attention to language. “Bollywood” films - feature-length movies produced and distributed in Bombay (Mumbai), India - are an excellent media source through which to examine linguistic anthropological topics of indexicality, bivalency, and identity in diasporic communities. In this paper, I analyze the circulation and consumption of Bollywood films - created in Hindi and subtitled in English - among South Asian-American (desi) communities in both Silicon Valley, CA and Queens, NY. Bollywood films are watched in family and peer groups, and portions of the films’ songs and dialogue become incorporated into everyday speech practices. I present and analyze instances of Hindi film dialogue interwoven into conversational exchanges between desi teens in ways that impact negotiations of style and identity. For many teens, the films provide narrative frameworks, prescripted dialogue, and socially recognizable registers and varieties of affect through which they enact their own dynamics of humor, flirting, conflict, and other types of talk. Drawing on ethnographic and sociolinguistic data, I contrast how these processes vary between these two diasporic communities.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.844
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.028
GPT teacher head0.247
Teacher spread0.219 · 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 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

Citations31
Published2015
Admission routes1
Has abstractyes

Explore more

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