Book Review: From Helping to Hand Grenades: Setting the Bar for Altruism
Bibliographic record
Abstract
suggests that “Kindness exists, but it struggles to stay afloat on an ocean of cruelty that is the default condition for organisms competing for existence on this planet ” (p. 9). The main premise of Barber’s book is that humans inherit a capacity for altruism that can be enhanced or diminished through nurture. Barber suggests that the core of this capacity evolved through kin selection and is reflected in parental investment. From this center, altruism ripples outward in concentric circles to reciprocity between members of ingroups, systems of cooperation in societies, and relations among nations. However, the larger the circle, the weaker the altruistic dispositions. In supporting this model, Barber adduces a potpourri of evidence drawn from a wide array of disciplines, including evolutionary biology, economics, political science, history, social and developmental psychology, game theory, anthropology, and neuroscience. By and large, this book is a good read for lay people and students, but we fear evolutionary psychologists will find many of the analyses simplified and compartmentalized, and some of the conclusions overgeneralized and sensationalized. We were also disappointed by Barber’s failure to define the central construct of the
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.021 |
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".