Love and Duty: Translating Films and Teaching Online Through a Pandemic
Bibliographic record
Abstract
The Chinese Film Classics project, launched in 2020, is an online research and teaching initiative aimed at making early Chinese films and cinema history more accessible to the general public. Led by Christopher Rea at the University of British Columbia, the project is centered on the website http://chinesefilmclassics.org and the companion YouTube channel Modern Chinese Cultural Studies. These two platforms together host new English translations of over two dozen Republican-era Chinese films, over two hundred film clips organized into thematic playlists, and a free online course of video lectures on Chinese film classics. This essay tells the story of how the Chinese Film Classics project grew from being a book project into a multiplatform translation, teaching, and publication project during the COVID-19 pandemic. Online teaching and social media publication involved multiple global storytellers: filmmakers, educators, translators, students, and the broader Internet public. How might moving things online change, or improve, the practice of cultural history? Rea highlights in particular the practical considerations facing the translator and gives examples of how, in a social media context, some of the stories are told not by creators and audiences but by data analytics.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".