Bibliographic record
Abstract
Abstract Investigations of “the face of the animal” have emerged primarily from the work of philosophers Emmanuel Levinas and Jacques Derrida and their respective anecdotes about the semiferal internment camp dog Bobby and Derrida’s domestic cat. If the face is the site where ethics comes into play, how can an ethics be worked out that takes those creatures we denominate “animals” into account? For a species who communicates primarily through human languages, the face is the prime semiotic surface, but for animals who communicate with one another through what we term calls, howls, scent marks, posture, or gesture, “the face” may be dispersed more generally over the body. In a reading of several face-to-face encounters in Sid Marty’s books Switchbacks: True Stories from the Canadian Rockies and The Black Grizzly of Whiskey Creek, this article analyzes the specific differences between bear faces and bare faces, asking questions pertaining to nakedness, animality, speciation and identity, transspecies relationships, and knowledge. What might happen if philosophy were taken out of the Parisian bathroom and into the Canadian Rocky Mountains? If Derrida’s cat poses questions such as the above, then those posed and the rejoinders made by the bears accompanying Marty through the back country also deserve attention.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.309 | 0.074 |
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".