‘Agents of Aggressive Order’: Letters, Hands, and the Grasping Power of Teeth in the Early Canadian Torture Narrative
Bibliographic record
Abstract
This paper brings together a most fascinating and under-examined body of early New World writing that belong to a genre of writing I call “the torture narrative” with the insights of Marshall McLuhan in order to offer a way of thinking about body parts, especially hands, teeth, tongues, and eyeballs, and their extensions through technologies such as alphabets, manuscripts, books, and weapons. At its core are questions about the nature and effects of the changes wrought by the early-Gutenberg era—a period characterized by vast scientific and technical discoveries, rising nationalisms, explorations, and conquests—in the New World. Rather than explore the ways an early New World print culture reflects or distorts discrete historical or ethnographic facts about colonial contact, I want to probe the ways the torture narrative speaks to the conditions of speaking and writing, and therefore to the discursive and textual production of the New World subject and New World space. My inquiry seeks to understand both the violence in, and the violence of, representation at founding historical moments, but its relevance far exceeds Renaissance or American studies. At stake in this discussion is an appreciation of a mode of intellectual inquiry that is attentive to the relationship between media technologies and the social worlds they reflect and transform.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.036 | 0.054 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".