Telling the Stories of Left-behind Children in China: From Diary Collection to Digital Filmmaking
Bibliographic record
Abstract
The issue of “left-behind children” in China has been widely recognized as a significant social problem, as more than 61 million children are living in villages away from their parents, who have migrated to large cities to seek employment opportunities. There is a very limited number of media products depicting left-behind children in rural China as central characters with individual personalities. As Stuart Hall states, representation is the process or channel or medium through which meanings are both created and reified. This paper analyzes how stories and voices of this underprivileged group are presented in recent years to the public in different non-fictional media forms, particularly documentary films. Through content analysis of selected samples, the paper examines how narratives are weaved about the lives and emotions of these children, and how the stories make sense of their family experiences. The paper discusses the power of digital narratives and visual-based expressions. It also examines how the products of representation are mediated by different types of storytellers, who are often motivated by a sense of social engagement to raise awareness about the plight of these children to appeal for support but addresses the issue from their specific perspectives. Image Credit: Still of Children at a Village School by Nengjie Jiang
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".