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‘He Is Not Here by Accident’: Transit, Sin and the Model Settler in Patrick Lussier’s Dracula 2000

2018· book-chapter· en· W2945172828 on OpenAlexaboutno aff
Jerod Ra’Del Hollyfield

Bibliographic record

VenueEdinburgh University Press eBooks · 2018
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsDraculaVampireMovie theaterHollywoodTrilogyArt historyArtColonialismHistoriographyHistoryLiteratureArchaeology

Abstract

fetched live from OpenAlex

This chapter examines Dracula 2000 as both a resistant text of settler colonial identity and an example of Hollywood’s influence on the Canadian film industry. Seen by Miramax cofounder Bob Weinstein as a potential franchise successor to the recently completed Scream trilogy, the film reunited much of that earlier franchise’s creative team with horror legend Wes Craven assuming the role of producer and passing directorial reigns to longtime Dimension editor and Canadian filmmaker Patrick Lussier. Focusing on a Dracula who is revealed to be an undead Judas Iscariot and a Van Helsing sustained through the 20 th Century by injections of Dracula’s blood, the film engages with a poststructuralist cycle of the settler/subject/colonial dynamic. Likewise, the film’s relocation from the imperial centre of London to New Orleans not only positions America as a contemporary imperial power but also harkens back to the port city’s legacy as a hub for slavery and global trade during Stoker’s time. Shot primarily on Canadian sound stages that doubled for London and New Orleans, Dracula 2000 also embodies contemporary production politics in which Hollywood’s relationship to settler nations such as Canada provides economic support for national cinemas while still dominating domestic box office.

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)0.002
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: none
Teacher disagreement score0.721
Threshold uncertainty score0.562

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0290.020
Scholarly communication0.0090.003
Open science0.0010.002
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.227
Teacher spread0.210 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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