Nigel Rothfels. <i>Elephant Trails: A History of Animals and Cultures</i>.
Bibliographic record
Abstract
People love elephants but have long struggled to understand them and determine what constitutes productive or moral behavior toward them. In the process, elephants have contended with changing human behavior, industrialization, the development of mass media and entertainment, colonization, and environmental change. Noted animal studies and history scholar Nigel Rothfels wades into these histories and debates with Elephant Trails: A History of Animals and Cultures. The book offers seven thematic chapters with case studies from the 1770s to the early twentieth century, many fascinating illustrations, and a helpful note on further reading. Rothfels’s volume explores the rich and varied history of people’s perceptions of elephants, suggesting that elephants have functioned as a kind of Rorschach test for many Europeans and North Americans. He thus examines some enduring ideas: elephants have exceptional wisdom and profound emotional depth; they endure immense suffering in captivity; they are, by turns, gentle beings appropriately employed around children or destructive monsters who deserve to be chained and punished; and, counterintuitively, they fear mice. To chart these ideas over time, the book offers a series of case studies that attend to the lived history of some elephant individuals—“Gunda, Alice, Josephine, Ned, Mena, Packy, Lily, and many more with and without names.” These studies reveal how often elephants defied human expectations and drove people to cover up the truth or deny what they saw with their own eyes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.023 |
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".