Bibliographic record
Abstract
This essay examines two films by the Chinese documentary filmmaker Wang Bing about temporary migrant workers in small, privately owned garment workshops in Zhejiang Province, China: Bitter Money (Ku Qian; 2016) and 15 Hours (Shi Wu Xiao Shi; 2017). Wang’s films portray Chinese garment workers’ lived experiences of “suspension,” as defined by Biao Xiang in this issue, in unique cinematic ways. Social sciences have paid close attention to the experiences of migrant workers, but art documentaries use audiovisual and aesthetic means to explore their everyday reality, producing what D. MacDougall calls distinctive “affective knowledge.” Wang’s films are usually categorized as part of the Sixth Generation of Chinese filmmakers, known for capturing social issues through observational methods. In this essay, I identify Wang’s works with the aesthetics of “slow cinema” and a global documentary trend in the visual arts as theorized by T. J. Demos in The Migrant Image. Based on close observation coupled with empathetic insight, Wang develops his own subjective method to portray people in a transformed and still changing China, where suspension is a common state of being. Ultimately, Wang’s films not only make the personal experiences of migrant workers visible and tangible, but also problematize their underlying, collective condition of suspension due to the contract labour system and associated hypermobility. The suspension approach suggests a productive way of bringing documentary art and social sciences into dialogue.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".