Bibliographic record
Abstract
This chapter consists of a short story, “Bandits”; it is followed by a reflective afterword, “Paragon of Animals.” The story is set in southwestern Ontario on a hobby farm, that quintessential nexus of urban and rural values, human and nonhuman relationships, where former urbanites, the Mulder family, care for nonhuman animals in the light of contemporary environmental concerns. This story involves the main character, Evan, attempting to humanely prevent raccoons from both “stealing” his laying hens’ eggs and killing the hens themselves. Written in the comic mode, the narrative explores the complexities of creature care and human responsibility within an ecologically redemptive framework. Using a phrase from Hamlet as a point of departure, the afterword explores in a familiar tone how a right understanding of humans as the paragon of animals, one characterized by humility and connection, can inform both literature and life. In this, the author’s thinking is influenced by Biblical texts, by a reading of Margaret Atwood’s poem “The animals in that country” and her novel Surfacing , and by the thinking of Wendell Berry, Marilynne Robinson, and Mary Oliver.
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 | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.312 | 0.111 |
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".