In Search of Authentic Borderlands: Zheng Junli and His Wartime Documentary <i>Long Live the Nation</i>
Bibliographic record
Abstract
The efforts of Chinese filmmakers during World War II have garnered increasing attention as more literary and visual materials continue to be unearthed. The era’s frequent displacements and nationalistic imperatives created a distinctive filmmaking landscape. This article delves into the director Zheng Junli’s quest for an authentic representation of China in his propaganda documentary Long Live the Nation (Minzu wansui) from 1943, which showcases the unity and diversity of six Chinese ethnic groups in the borderlands. One of Zheng’s central concerns in the filmmaking process was zhenshi (authenticity), which I put into a broader context of individual travel experiences and cultural interaction. Factors such as Zheng’s previous film practices, resource limitations, daily travel encounters, and desire to portray reality contributed to his documentary decisions. Foregrounding Zheng’s individual experiences, the article contends that the process of making this travel documentary involved negotiation and compromise between the traveler and the destination, culminating in a performative authenticity that depicts a united China unfolding on screen. The study sheds light on the complexities involved in crafting wartime travel propaganda documentaries and proposes the importance of studying travel and mobility to understand the artistic creations during the wartime period.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".