Bibliographic record
Abstract
Introduction to Popular Asian Cinemas by Anne T. Ciecko (University of Massachusetts-Amherst, USA) 1. Film Theory and Asian Cinemas by Anne T. Ciecko 2. Indonesia by Krishna Sen (Curtin University of Technology, Australia) 3. Malaysia by William van der Heide (University of Newcastle, Australia) 4. Singapore by Jean Jan Uhde Yvonne Ng Uhde (University of Waterloo, Canada) 5. Vietnam by Panivong Norindr (University of Southern California, USA) 6. Thailand by Anchalee Chaiworaporn Adam Knee (Film critic, Bangkok, Thailand Ohio University, USA) 7. Philippines by Jose B. Capino (Ateneo de Manila University, Philippines) 8. India by Corey K. Creekmur Jyotika Virdi (University of Iowa, USA University of Windsor, Canada) 9. Sri Lanka by Wimal Dissanayake (University of Hawaii, USA) 10. Bangladesh by Zakir Hussain Raju (Independent University, Bangladesh) 11. Korea by Hyangjin Lee (University of Sheffield, UK) 12. Mainland China by Augusta Lee Palmer (Brooklyn College, USA) 13. Taiwan by Emilie Yueh-yu Yeh (Hong Kong Baptist University) 14. Hong Kong by Anne T. Ciecko 15. Japan by Darrell William Davis (University of New South Wales, Australia) Conclusion
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.003 |
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".