Om Puri: The man who presented the real faces of the subcontinent of India
Bibliographic record
Abstract
The Indian film industry continues to turn out between 1600 and 2000 films every year, making it the largest movie-producing country in the world. Yet, it would be a challenge for an average European or American moviegoer to name a film actor from the Indian subcontinent. Naming the films may be easier. For instance, millennials may be able to name Slumdog Millionaire (2008), Generation X crowd may mention Gandhi (1982) and the older audiences may recall The Party (1968) and Ganga Din (1939) as movies about the Indians and India. It was not until the movie Gandhi that Indian actors were allowed to play as Indians. Sam Jaffe and Abner Biberman played as Indians in Ganga Din; Peter Sellers was the Indian actor in The Party, and Shirley MacLaine was the Princess Aouda in Around the World in 80 Days (1956). It is reasonable to assume that many film viewers may be unfamiliar with Om Puri, an actor who played in over 325 films in India, Pakistan, the United Kingdom and the United States, and made films in English, Bengali, Punjabi and Tamil languages. Om Puri passed away in 2017. His name may be unfamiliar, but his face and his work as an actor will remain unforgettable. Between Gandhi (1982) and Viceroy’s House (2017), Puri acted in two dozen films in the United Kingdom, Canada and the United States. This article discusses Puri’s work in popular Hindi cinema, in Indian Parallel Cinema, and European and North American films.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 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".