Bibliographic record
Abstract
Modern zoos and aquaria are sites of education, recreation, research, and conservation; they are also the result of thousands of years of human-animal interaction, colonial expansion, and imperial violence. Beginning with the similar but not identical menagerie, this paper historically situates customs and ideologies to demonstrate how the zoo genre has changed over time. A brief review of academic, grey, professional, and community literature explores critical perspectives on contemporary animal display practices. Though North American zoos have come a long way from the concrete pits and metal bars of the 19th and 20th centuries—and it is important to recognize that the people working in zoos and aquaria do critically important work—I argue that contemporary zoo exhibitions still act as a manifestation of these legacies when they remove tangible reminders of human contact and interference with animals and other non-human beings. Through an analysis of exhibit design at a zoo and an aquarium in Canada, this paper explores how animal exhibits at zoos and aquaria may maintain a colonial gaze in their dominant positioning of human visitors. Elder Albert Marshall’s “Two-Eyed Seeing” framework shows us how we might reimagine these practices with both Indigenous and mainstream Western ways of knowing.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.017 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".