Exploring the Effects of Violating the 180-Degree Rule on Film Viewing Preferences
Bibliographic record
Abstract
The 180-degree rule is thought to help smooth the change between film shots. When two individuals are speaking to each other, there is an imaginary axis of action running between them. If the camera crosses this axis, it breaks the 180-degree rule. A violation of the 180-degree rule is thought to have negative effects on viewers’ enjoyment of films. The present study investigated this idea. Experiment 1 established that naive participants can detect violations in videos. Experiment 2 tested the putative negative effects of 180-degree rule violations. The results indicated that violations can confuse and disorient viewers. Critically, as revealed by Experiment 3, violations did not alter the viewers’ liking of a video: Viewers were as likely to prefer a video with a 180-degree violation as one without. Collectively, these data shed light on fundamental beliefs regarding the 180-degree rule, which may help inform filming decisions around film enjoyment.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".