The Original Industrial Revolution. Did Cold Winters Select for Cognitive Ability?
Why this work is in the frame
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Bibliographic record
Abstract
Rushton and Jensen argued that cognitive ability differs between human populations. But why are such differences expectable? Their answer: as modern humans spread out of Africa and into northern Eurasia, they entered colder and more seasonal climates that selected for the ability to plan ahead, in order to store food, make clothes, and build shelters for winter. This cold winter theory is supported by research on Paleolithic humans and recent hunter-gatherers. Tools become more diverse and complex as effective temperature decreases, apparently because food has to be obtained during limited periods and over large areas. There is also more storage of food and fuel and greater use of untended traps and snares. Finally, shelters have to be sturdier, and clothing more cold-resistant. The resulting cognitive demands are met primarily by women because the lack of opportunities for food gathering pushes them into more cognitively demanding tasks, like garment making, needlework, weaving, leatherworking, pottery, and kiln operation. The northern tier of Paleolithic Eurasia thus produced the “Original Industrial Revolution”—an explosion of creativity that preadapted its inhabitants for later developments, i.e., farming, more complex technology and social organization, and an increasingly future-oriented culture. Over time, these humans would spread south, replacing earlier populations that could less easily exploit the possibilities of the new cultural environment. As this environment developed further, it selected for further increases in cognitive ability. Indeed, mean intelligence seems to have risen during recorded history at temperate latitudes in Europe and East Asia. There is thus no unified theory for the evolution of human intelligence. A key stage was adaptation to cold winters during the Paleolithic, but much happened later.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it