Bibliographic record
Abstract
This book contains several dozen true stories about the diverse ways that animals and humans interact. “The stories in this collection explore what animals mean to us … as our companions and our workmates, our symbols and our totems, our possessions and our food,” writes editor and compiler Pam Chamberlain in her Introduction. Many different animals fill the pages of this book — pets, farm animals, and wild creatures, including frogs and salamanders. The authors, mostly Canadian, are journalists, farmers, hunters, conservationists, artists, veterinarians, biologists, teachers, sociologists, and poets. Thumbnail sketches and photos of each author are included at the back of the book. The stories elicit many emotions. In “Freeing the Pike,” Richard Wagamese takes us on a spiritual journey on a summer day in his lonely childhood. Aboriginal by birth, but adopted into a white family, he describes the powerful connection he feels to the large fish he catches and releases back into the river. “For the Indian that lived in me, that fish was honour and respect and love.” Iconic Canadian author Farley Mowat laments the loss of a beloved dog in “April Passage.” “The pact of timelessness between the two of us was ended, and I went from him into the darkening tunnel of the years.” Many stories are light-hearted and entertaining. In “My Summer with Russell Crow,” Karin Melberg Schwier describes how a young crow befriends and charms her and her husband in their backyard and then abandons them as unexpectedly as he arrives. In “Old One-eye,” farmer and naturalist Paul Beingessner recalls his bizarre role as bodyguard to an unusual “cyclopean” rooster. “It seemed that his manliness had been removed along with the eye that so offended the one who had plucked it out.” While most of the stories in this collection are a pleasure to read, a handful examines the darker side of our dealings with animals. For example, in “Pretty Bird,” Andrea Miller questions the ethics of capturing and smuggling parrots into faraway countries where they are often kept in solitary confinement, a particularly cruel fate for a creature that is highly social and gregarious in the wild. Although I don’t usually read short stories, this collection exemplifies the pleasure of the short read. This book would make an ideal gift for readers who are close to animals and enjoy relaxing in their company.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.018 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".