Bibliographic record
Abstract
Michael Lundblad (2009) has recently called for an “animality studies” reading of Jack London’s The Call of the Wild (1903), contending that this approach to this well-known text would reveal “a more complex, unsettled and inconsistent engagement with the question of the animal and constructions of the human than we might otherwise assume” (p. 498). Echoing the politics of stereotype endured by Black Beauty and other equines, London’s sled dogs are burdened with not only Western civilization’s assumptions about their species, but also our assumptions about wolves as the embodiment of both wildness and wilderness, which London himself was essential in establishing. While Lundblad’s examination focuses on the relationship of animality and sexuality in particular, it nevertheless correctly identifies a general oversight in critical analyses of London’s canine canon. These works include The Call of the Wild , “Bâtard” (1904), White Fang (1905), and his often-overlooked nonfiction ode to sled dogs, “Husky — The Wolf Dog of the North” (1900). This analysis, however, can be expanded to include filmic adaptations of London’s writing, including both those that adhere closely to the source text and those which omit the main human characters and the plot, to borrow only the setting and the dog. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 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 | 0.006 | 0.034 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".