Bibliographic record
Abstract
What constitutes a psychological thriller, and the key developments in film production and reception that have encouraged this category of filmmaking to emerge, has been the subject of much debate within Cinema Studies. This project details the ways in which the psychological thriller in many ways exceeds conventional notions of genre and auteur cinema. Furthermore, it argues that examining films made during the 1990s offers an especially rich ground for expanding the definitions of the psychological thriller to account for critical reception in an internet age. I am interested in how films that get referred to as psychological thrillers in the 1990s became characterized as such through their positioning as “indies” and by the high level “buzz” they generated among critics and preview audiences, which encouraged audiences to engage in multiple viewings of these films both in theaters and at home. While narrative and aesthetic devices have been a primary focus of scholars writing on these films, I am interested in how dynamics of reception equally contributed to the films’ mind-bending qualities. The significance of repetition at both the level of the text and in viewing of these films continues to be seen as a central characteristic of psychological thrillers today. I suggest that more recent films, namely Us (Jordan Peele, US, 2019) and Enemy (Denis Villeneuve, Canada and Spain, 2013), further elaborate these dynamics through their innovative use of doppelganger figures.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".