Family Dynamics in the Representation of Childhood in Horror Film Trailers: A Cross-Cultural Analysis
Bibliographic record
Abstract
This research examines how child characters are represented within the context of family dynamics in global horror cinema, focusing on ten diverse horror film trailers from the past decade. Utilizing an analytical framework that encompasses cinematography, lighting, settings, symbolic imagery, sound effects, music, semiotics, interaction, and narrative context, the study draws on purposive sampling from regions including the United States, Canada, Australia, China, South Korea, Japan, India, Iran, Spain, and Scandinavia. This approach highlights the cultural nuances that shape the portrayal of children in horror. Grounded in psychoanalytic film theory and cultural semiotics, the study identifies recurring themes, motifs, and storytelling techniques while exploring the implications of human desire and curiosity within horror narratives. Key symbolic elements, such as dolls, mirrors, and color choices, serve as powerful storytelling devices. The interplay of cinematography, sound design, and symbolic imagery creates an emotional impact rooted in fear, suspense, and contemplation of dark themes. This research enriches psychoanalytic film theory and cultural semiotics, offering theoretical insights into the psychological and symbolic dimensions of horror narratives. By unraveling the relationship between innocence and horror, the study reveals how filmmakers strategically employ visuals and narratives to evoke fear and engage audiences.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".