Bibliographic record
Abstract
“Animals are such agreeable friends – they ask no questions, they pass no criticisms,” George Eliot wrote long ago. Often we believe, because we love an animal very much, that we are loved in return. This usually applies to companion dogs and cats, who are limited to one section in this chapter because such human–pet relationships are so common and universal. But that our animal friends love us can be a dangerous assumption. Dale Lott’s (2002) mother, who thought that she and her horse Smoky were great buddies, was killed when he suddenly threw himself backward so that the saddle horn was driven into her heart. Lott also describes a rancher in Idaho who raised a bull from a calf. Even when the animal was full-grown, he would let the man pet him and climb onto his back. Then one day the bull killed the man, mutilating his body and refusing to allow it to be removed from the corral. Lott writes that bison, for example, are “immune to our charm, sincerity, personal integrity and peaceful intentions,” no matter what we may think. One man, who kept a few pet bison, was stunned one day when a young bull attacked him. The upward thrust of his horn into the man’s belly and ribs was so strong that it chucked him over the fence where a veterinarian was able to save his life. We cannot ever know if people and animals, even reptiles, can be best buddies. The people may think they are, but their buddy may “think” otherwise, as the above examples indicate. The first four relationships described in this chapter about Elsa, the lion, mountain gorillas, olive baboons, and sociable whales do seem to depict true friendship. The animals are free to stay or leave the people they relate to, so there is equality between them. The second type of example includes horses and elephants who work for individual people. These animals and their human exploiters may have a good relationship, but it is not equitable. The animals must do what the humans expect and want. The same is true for the wolf Brenin and the hyrax Tsavo (and dogs), examples of the third type, but these animals, at least, were not expected to work for their keep. They did not try to escape from their close human buddies but were still subject to their humans’ whims. The fourth and final type includes individuals who were subject to close human supervision by researchers devoted to teaching them to learn information that presumably exhibited their intelligence. These animals had no life of their own, although they were very close to their teachers who spent thousands of hours with them, drilling them on subjects important to people. But they were locked up at night when their work was done.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".