Bibliographic record
Abstract
CINEFAN FESTIVAL OF ASIAN CINEMA IN DELHI 2005 Back in 1999, when the First Cinefan Festival of Asian Cinema was launched in New Delhi by publicist Aruna Vasudev, together with UNESCO friends and with the support of Delhi Chief Minister Sheila Dikshit, the program numbered only 27 Asian films. Seven years later, in partnership with entrepreneur Neville Tuli and his Indian auction house for popular art and Hollywood-Bollywood memorabilia, the re-christened 7th Ocean's Cinefan Festival of Asian Cinema (15-24 July 2005) could offer its public of feast of 121 Asian films from 35 countries. The films were presented on four screens in the Siri Fort Complex, plus an extra venue at the Alliance Française. The evening shows were nearly all sold out. How did Delhi become one of the biggest and most representative festivals of Asian cinema? Only Pusan in Korea and Filmex in Tokyo are comparable oases of...
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.463 | 0.284 |
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".