Bibliographic record
Abstract
What happens when a horror movie monster looks or appears to look at you, the viewer?What does this look do to the filmic fiction?It is often the case in the horror film where the monster is framed looking at the camera.Despite its prevalence in the genre, the returned gaze is often dismissed or forgotten in horror.By adopting a cognitive methodology that draws on both narratological and psychological theories, I argue that, when the gaze is returned in horror films, affects of unease or discomfort are produced.As they do this, they do not rupture the cinematic illusion or our emotional engagement because cinema is not an illusion to begin with.Ultimately, the returned gaze is a formal choice in horror that is rooted in certain innate human behaviours.I analyse various instances of the returned gaze in Halloween (1978) and The Shining (1980) to demonstrate how this phenomenon in horror engages us.Chapter 2: Characterizing the Returned Gaze in Horror…………………….. 36 Defining the Horror Context……………………………………………………... 36 Illusion, Alienation and Emotional Engagement………………………………… 39 Identifying the Returned Gaze in Horror……………………………………….... 50 The Communicative Gaze and the Role of Viewer Knowledge………………….54 Chapter 3: Looking From a Distance in Halloween…………………………… 59 Carol Clover's Forgotten Argument: The Prevalence of Eyes in Horror………… 60 The Narrative Construction of the Monster and the Importance of Faces……….. 62 Regulating the Intensity of the Returned Gaze…………………………………… 66 The Returned Gazes in Halloween………………………………………………… 72 Chapter 4: Ghostly Looks in The Shining……………………………………….. 81 The Production of Fear and Unease via Returned Gaze………………………….. 84 The Affective Nuances of the Returned Gaze…………………………………….92
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".