Bibliographic record
Abstract
Section 1: Nature and Evolution Topic 1: Evolution and human 1.1. Excerpts from On Nature E. O. Wilson 1.2. On human D. L. Hull 1.3. Excerpts from Adapting Minds David J. Buller 1.4. A plea for human Edouard Machery 1.5. What is a human universal? behavioral ecology and human Elizabeth Cashdan Topic 2: A Stone-Age Mind? 2.1. Evolutionary psychology: A primer Leda Cosmides and John Tooby 2.2. The basic components of the human mind were not solidified during the Pleistocene epoch Stephen Downes Topic 3: Innateness 3.1. Core knowledge Katherine D. Kinzler and Elizabeth S. Spelke 3.2. What is innateness? Paul E. Griffiths 3.3. Innateness in cognitive science Richard Samuels Topic 4: Genetic Determinism 4.1 Genetic influence on human psychological traits: A survey Thomas J. Bouchard 4.2. Behavioral development and Darwinian evolution Patrick Bateson 4.3. Battling the undead: How (and how not) to resist genetic determinism Philip Kitcher Section 2: and Diversity Topic 5: Universals, Individual Variation and Cultural Variation 5.1. Excerpts from Universals Donald Brown 5.2. The weirdest people in the world? Joe Heinrich, Steven J. Heine, and Ara Norenzayan 5.3. On the universality of human and the uniqueness of the individual: The role of genetics and adaptation James Tooby and Leda Cosmides 5.4. Culture and cognition Daniel M. T. Fessler and Edouard Machery 5.5. Exceprts from Not by Genes Alone Peter J. Richerson and Robert Boyd 5.6. The informational commonwealth Kim Sterenley Topic 6: Social Construction 6.1. The politics of menopause: The discovery of a deficiency disease Frances B. McCrea 6.2. The looping effects of human kinds Ian Hacking 6.3. The odd couple: The compatibility of social construction and evolutionary psychology Ronald Mallon and Stephen Stitch Topic 7: Genetic Diversity 7.1. The apportionment of human diversity Richard Lewontin 7.3. genetic diversity: Lewontin's fallacy A. W. F. Edwards 7.4. Genetic structure of human populations N. A. Rosenberg Topic 8: Races 8.1. A social constructionist analysis of race Sally A. Haslanger Individual ancestry inference and the reification of race as a biological phenomenon Dan Bolnick Topic 9: Sex 9.1. Excerpt from The Evolution of Sexuality Donald Symons 9.2. Excerpt from Nature and the Limits of Science John Dupre 9.3. Same-sex sexual behavior and evolution Nathan W. Bailey and Marlene Zuk Section 3: Nature and Normality Topic 10: Health 10.1. Health as a theoretical concept Christopher Boorse 10.2. Against normal functions Ron Admunson 10.3. Mental Health and Disorder R. C. Cooper Topic 11: Politics and the Concept of Nature 11.1. A fatal attraction to normalizing Anita Silvers 11.2. Human nature and its role in feminist theory L. M. Antony 11.3. Is human important for feminism? Nancy Holstrom Topic 12: Trans-humanism 12.1. Ageless bodies, happy souls: Biotechnology and the pursuit of Perfection Leon Kass 12.2. In defense of posthuman dignity Nick Bostrom
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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.026 | 0.178 |
| Scholarly communication | 0.031 | 0.024 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.015 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 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".