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The Words Change Everything: Haunting, Contagion and The Stranger in Tony Burgess’s Pontypool

2018· article· en· W2900945494 on OpenAlexaffabout
Evelyn Deshane, R. Travis Morton

Bibliographic record

VenueLondon Journal of Canadian Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsZombieFeelingIdeologyScholarshipInsiderSociologyLyricsHistoryAestheticsLiteratureMedia studiesPsychologyLawPhilosophyArtPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

In 2018, O Canada ’s lyrics were made gender neutral. This change comes at a time when certain key public figures refuse to use gender neutral language. The linguistic tension and ideological divide within Canada creates a haunted feeling around certain minority groups, leaving everyone feeling out of place. This article examines how viral ideas and word choices spread through media technologies via the ‘word virus’. We use the figure of the zombie to show how the word virus becomes bad ideology, one that spreads and takes over certain spaces and enacts the presence of the insider/outsider. To reflect on ‘word viruses’ gone awry, we borrow and build on scholarship from the emerging field of hauntology made popular by Jacques Derrida and Avery Gordon. Ultimately, we present Tony Burgess’s horror novel Pontypool Changes Everything turned Canadian horror film Pontypool as a speculative case study, since Burgess’s texts suggest that what is more infectious than the zombie-outsider is the insider’s own language, which identifies and labels the outsider. By positing a possible cure for the word virus within Pontypool , the film adaptation suggests that the ways in which we cease becoming infected with bad ideas is not to stop speaking or isolate ourselves through quarantine, but deliberately seek out the stranger in order to challenge and change the meaning of words.

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)0.003
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.039
GPT teacher head0.312
Teacher spread0.273 · 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
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

Citations3
Published2018
Admission routes2
Has abstractyes

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