Embodied Figuration and Character Emotion in Cinema: The Role of Embodied Affective Cues
Bibliographic record
Abstract
Abstract This paper engages with research on embodiment, cognition, and figurative meaning in cinema to develop a system for categorizing the cinematic cues that viewers use to gauge character affective experience. Many film theorists whose work is primarily focused on affective experience consider the relationship between the spectator and the character’s facial and bodily expressions. However, films often convey the affective states of characters by integrating bodily expressions with cinematic techniques, such as lighting, framing, and editing. These cinematic techniques can provide insight into a character’s affective states through their figurative associations with represented moods, feelings, and emotions. By exploring the metonymic, metaphoric, and similative properties of cinematic representations of affect, we can better understand how cinematic representations are understood by a community of viewers. Throughout this paper, I put forth the framework of “embodied affective cues” and identify behavioral, physical, and environmental cues to address how a character’s affective experience is represented by cinematic cues external to their body.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".