Bibliographic record
Abstract
This essay examines A. R. Rahman’s film music through a comparative study of aspects of Indian and Western musical and cinematic conventions, and analyses five of Rahman’s scores: two collaborations with Tamil director Mani Ratnam, ( Roja , 1992 and Bombay , 1995), one collaboration with Indian-Canadian director Deepa Mehta ( Fire , 1996), and two collaborations with Danny Boyle ( Slumdog Millionaire , 2008 and 127 Hours , 2010). These films together represent important aspects of Tamil, Hindi, multinational and Western cinemas, and Rahman’s naturally multicultural approach to composition offers an ‘alternate space’ that is key to his ability to imbue his scores with meaning that successfully complements filmic narrative across cultures. His soundtracks draw on scoring conventions that translate from East to West, blending traditional Indian instruments and tonality with Western symphonic instruments and musical structures, and electronic layers and beats with melodies articulated in his own Eastern singing style. Through analysis of the diverse musical aspects of his oeuvre in both Indian and international contexts, this essay offers insights into how Rahman’s film music so successfully bridges the cultural and aesthetic divide between East and West. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".