Bibliographic record
Abstract
The human propensity to take an ethical stance toward oneself and others is found in every known society, yet we also know that values taken for granted in one society can contradict those in another. Does ethical life arise from human nature itself? Is it a universal human trait? Or is it a product of one's cultural and historical context? This book offers a new approach to the empirical study of ethical life that reconciles these questions, showing how ethics arise at the intersection of human biology and social dynamics. Drawing on the latest findings in psychology, conversational interaction, ethnography, and history, the book takes readers from inner city America to Samoa and the Inuit Arctic to reveal how we are creatures of our biology as well as our history—and how our ethical lives are contingent on both. The book looks at Melanesian theories of mind and the training of Buddhist monks, and discusses important social causes such as the British abolitionist movement and American feminism. It explores how styles of child rearing, notions of the person, and moral codes in different communities elaborate on certain basic human tendencies while suppressing or ignoring others. Certain to provoke debate, the book presents an entirely new way of thinking about ethics, morals, and the factors that shape them.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 0.010 |
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".