Bibliographic record
Abstract
This article weirdly pairs the cannibal and the plagiarist as moral monsters – figures outside of conventional societal and legal categories – to argue that monstrous logic can offer potential correctives to common laws that otherwise demonize transgressive figures. An introduction to Mark Fisher's analysis of the “weird” will complement the tracing of the figure of Richard Parker throughout three literary texts and one historical text: Edgar Allan Poe's The Narrative of Arthur Gordon Pym of Nantucket, Yann Martel's Life of Pi, Mat Johnson's Pym, and A.W. Brian Simpson's Cannibalism and the Common Law. Demonstrating the familiar and repeated tropes associated with nautical survival cannibalism, or “the custom of the sea,” this article claims the economic and political motivations that often underlie travel narratives relegate any monstrous and humanist concerns to a secondary, lesser importance. The conclusion suggests that thinking through monstrous categories may offer alternative solutions to contemporary problems of conspicuous consumption, resource ownership and exhaustion, and environmental crisis.
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 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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.015 | 0.073 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 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".