Bibliographic record
Abstract
Foreword - ishtiaq Ahmed Introduction - Anjali Gera Roy Part 1: BRAND BOLLYWOOD AND THE NEW BOLLYWOOD FILM Mainstream Hindi Cinema and Brand Bollywood: The Transformation of a Cultural Artifact - M K Raghavendra Post-national B(H)ollywood and the National Imaginary Meena - T Pillai Part 2: BOLLYWOOD'S SOFT POWER: SOME FACTS AND FIGURES Bollywood and Soft Power: Content Trends and Hybridity in Popular Hindi Cinema - David J. Schaefer and Kavita Karan A Regional Mosaic: Linguistic Diversity and India's Film Trade - Sunitha Chitrapu Part 3: INDIAN FILMS' TRADITIONAL MARKETS: SOUTH ASIA, SOUTHEAST ASIA, AFRICA, AND RUSSIA Dada Negativity and Pakistani Characters in Bollywood Films - Kamal ud Din and Nukhbah Taj Langah Soft Power and Pakistani Viewers - Shahnaz Khan Bollywood Film Culture in Indonesia's Mediascapes Shuri Mariasih - Gietty Tambunan Indian Films in the USSR and Russia: Past, Present, and Future Elena - Igorevna Doroshenko Indophilie and Bollywood's Popularity in Senegal: Strands of Identity Dynamics - Gwenda Vander Steene Bollywoodization as (H)Indianization? Bangladesh Film Industry under National Protection - Raju Zakir Hossain Part 4: NEW TERRITORIES: BOLLYWOOD IN THE WEST AUSTRALIA, CANADA, EUROPE, AND NEW ZEALAND From Tawa'if to Wife? Making Sense of Bollywood's Courtesan - Genre Teresa Hubel Bollywood in da Club: Social Space in Toronto's South Asian Community - Omme-Salma Rahemtullah Bollywood Internet Forums and Australian Cultural Diplomacy - Andrew Hassam Addressing the Nonresident: Soft Power, Bollywood, and the Diasporic Audience - Adrian Athique Bollywood's Circuits in Germany - Florian Krauss Imdex
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.070 | 0.009 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".