Bibliographic record
Abstract
Abstract Survival of the Virtuous offers an account of how moral traits evolved in the human species. It explains why we are not necessarily bad by nature, why we are not evolved to look out only for number one, and why nice guys need not finish last. It offers an account of how virtuous behaviors such as altruism, justice, honesty, loyalty, self-control, purity, and respect for authority evolved in our species (and in other species as well). It argues that the key to solving puzzles of morality such as what it is, how we acquire moral traits, why we sometimes behave badly, and how we make moral decisions lies in figuring out what adaptive functions moral traits served in early human environments and how they are influenced by social learning, culture, and strategic social interactions in the modern world. It offers evidence that the primary function of virtuous behaviors is to enable individuals to advance their interests by cooperating with others and that moral decision-making mechanisms evolved and develop in a Russian doll manner. Uniquely human “new brain” mechanisms that enable us to make moral decisions in rational ways evolved on top of “old brain” mechanisms that induce us (and some other animals) to make moral decisions in more emotional-intuitive ways. Although we tend to become increasingly rational as we develop, we retain the capacity to make moral judgments in primitive ways, and reason is a tool that can be used for good or evil.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".