Bibliographic record
Abstract
At the core of Jeff Lemire’s 2009-2013 graphic novel Sweet Tooth is a tale of resource extraction. This may seem an extraordinary claim to make about a dark fairy tale of a text that is chiefly concerned with the postapocalyptic relationship between an ageing hockey bruiser and a young deer-human hybrid boy. Yet Sweet Tooth #26-8 and #35 reveal that both the mysterious plague responsible for destroying human civilization in the world of Lemire’s story and the animal hybrid children inexplicably born in the same era have their origins in an ancient nonhuman force disinterred from the Arctic ice. The trope of nonhuman horrors unleashed from drilling or melting in the Arctic has become a common one; Sweet Tooth’s world-ecological (Moore) framing of the incident as one that brings together colonialism, capitalism, and science is not particularly unique. However, the Canadian Lemire’s refusal to differentiate between genetic science and indigenous cosmology in the comic’s portrayal of both viral pandemic and embodied animal gods offers the possibility of a geontological (Povinelli) reading. Here, I interpret Sweet Tooth as fundamentally a piece of climate change fiction— one that responds to an urgent need for new ways to understand a world that is not only post-Human, but also post-Life.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".