The Words Change Everything: Haunting, Contagion and The Stranger in Tony Burgess’s Pontypool
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.023 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".