The New Breed: What our History with Animals Reveals About our Future with Robots by Kate Darling
Bibliographic record
Abstract
ROBOTS WERE ONCE RELEGATED to roles that were “dirty, dull, or dangerous,”3 such as welding parts on car assembly lines, but today, they occupy more visible spaces in our workplaces, homes, and public areas. This visibility has provoked questions frequently seen in media inciting moral panic: Will robots cause job loss? Will robots become sentient? In The New Breed: What our History with Animals Reveals About our Future with Robots (“The New Breed”), Kate Darling explains that these fears are misplaced and that our tendency to anthropomorphize robots fosters false determinism. Darling imagines a different kind of agency, drawing on our historical relationships with animals, to shape future thinking about robotic technology. Reflecting on robots as a new breed or strain allows us to envision them as ontological interpolations rather than human-replacements.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".