Finding a home for the video essay: Videographic criticism and the study of Chinese television drama
Bibliographic record
Abstract
Although some conflate the digital humanities with data-driven quantitative research, other conceptualizations of the digital humanities emphasize post-print methods of scholarship with unprecedented reach outside of the academy. Video essays are an example of this vision for the digital humanities: they are critiques of film advanced through film itself with crossover appeal to popular audiences. This paper places the author’s personal experience creating a video essay on Chinese television in the context of the state of the field of Chinese television studies and current debates over the function of videographic criticism. Current scholarship on Chinese television often neglects the visuality of the televisual text and the individual experience of television viewing. Because video essays are themselves an audiovisual text, they foreground the visual, and their position between popular and scholarly forms of communication creates room for including subjective responses. The critical reflexivity of ‘scholarly video essays’ on Chinese television would serve as a useful complement to the vernacular audiovisual commentary already prevalent on the Chinese internet. By reconceptualizing the work and proper position of the scholar, videographic criticism can productively challenge the ‘regime of separation’ between researcher and object of study upon which East Asian studies is built.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".