Bibliographic record
Abstract
Don LePan’s novel Animals, a dystopian account of a future gone wrong, is an animal story with an ironic twist. Featuring no actual animals – indeed, set at a time when there are virtually no animals left on the planet, Animals is driven by narrative tensions that disrupt enduring forms of speciesism and highlight the simultaneous necessity and impossibility of such categories. The novel converges with the efforts of posthumanist critics like Cary Wolfe, Jodey Castricano, and Donna Haraway in its depiction of the human/non-human divide and in its insistence on the philosophical necessity of including non-human animals in the designation of the Other to whom we remain morally responsible. In this sense, Sam’s experience of becoming mongrel – his descent from human, to mongrel, to raw material for consumption – epitomizes the broader dehumanization of an entire culture. By inviting readers to judge the decisions characters make in reinforcing and policing the constructed categories of mongrels and humans, LePan questions the unstable classificatory systems through which we organize physical and textual worlds.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".