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Record W6986623504

The Puzzling Rollercoaster of the Psychological Thriller
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2020· other· en· W6986623504 on OpenAlexaboutno aff

Bibliographic record

VenueSUNY Digital Repository Support (State University of New York System) · 2020
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFilmmakingMovie theaterNarrativeSubject (documents)Repetition (rhetorical device)Film studiesGermanFocus (optics)
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.304
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.188
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

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