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
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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.
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 teacher head, 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".