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Record W1914649601 · doi:10.29173/mruhr20

The Relocation of Monstrosity: An Analysis of Horror in Brian De Palma’s Carrie

2013· article· en· W1914649601 on OpenAlexaffvenue
Mike Thorn

Bibliographic record

VenueMount Royal Undergraduate Humanities Review (MRUHR) · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCinema and Media Studies
Canadian institutionsMount Royal University
Fundersnot available
KeywordsIconographyArtAlterityTransformative learningNormativeAestheticsLiteratureArt historyPhilosophySociologyTheologyEpistemology

Abstract

fetched live from OpenAlex

The presence and expression of monstrosity, violence, and terror confirm Carrie’s status as a horror film. It is traditional in its use of visual cues such as darkness, blood and abnormality as sources of fear. In this sense, it obeys the conventions of its genre and embraces its iconography. However, the initially normative and ultimately transformative examination of monstrosity solidifies its position as an atypical horror film. The protagonist’s normalized environment is revealed to be a sinister setting, to the extent that its monstrosity becomes contagious. Carrie’s alterity is not inherently terrifying; she is pushed to terrifying measures by the social body that victimizes her.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.007
Scholarly communication0.0080.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.031
GPT teacher head0.237
Teacher spread0.206 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations1
Published2013
Admission routes2
Has abstractyes

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