Cetaceans in the City: Orca Captivity, Animal Rights, and Environmental Values in Vancouver
Bibliographic record
Abstract
Animal Metropolis brings a Canadian perspective to the growing field of animal history, ranging across species and cities, from the beavers who engineered Stanley Park to the carthorses who shaped the city of Montreal. Some essays consider animals as spectacle: orca captivity in Vancouver, polar bear tourism in Churchill, Manitoba, fish on display in the Dominion Fisheries Museum, and the racialized memory of Jumbo the elephant in St. Thomas, Ontario. Others examine the bodily intimacies of shared urban spaces: the regulation of rabid dogs in Banff, the maternal politics of pure milk in Hamilton and the circulation of tetanus bacilli from horse to human in Toronto. Another considers the marginalization of women in Canada’s animal welfare movement. The authors collectively push forward from a historiography that features nonhuman animals as objects within human-centered inquiries to a historiography that considers the eclectic contacts, exchanges, and cohabitation of human and nonhuman animals. With contributions by: Kristoffer Archibald, Jason Colby, George Colpitts, Joanna Dean, Carla Hustak, Darcy Ingram, Sean Kheraj, William Knight, Sherry Olson, Rachel Poliquin, and Christabelle Sethna
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".