The Medium Is the Monster: Canadian Adaptations of Frankenstein and the Discourse of Technology
Bibliographic record
Abstract
The question that animates this book might at first sound like the start of a joke: what do modern technology, Mary Shelley’s Frankenstein, and Canada have to do with one another? The short answer is “Marshall McLuhan,” and much of what follows will be devoted to explaining this punchline. I want to venture a twofold argument: first, that Shelley’s Frankenstein effectively “reinvented” the meaning of the word “technology” for modern English; and second, that Marshall McLuhan’s media theory, together with its receptions in Canadian popular culture and abroad – constitute a tradition in adaptations of Frankenstein that has globalized this Frankensteinian sense of the word. So my two main tasks here are to provide a concrete account of the historical origins and transformation of the definitively modern word “technology,” and, by closely reading Frankenstein and its Canadian adaptations, many of which also adapt McLuhan, to model new directions for adaptation studies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.020 | 0.031 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".