Bibliographic record
Abstract
Human intelligence is something of a minefield. Here, I mean ‘intelligence’ in the sense of intellectual ability, and not in the sense of secret information collected by spies—although that too is a minefield. The very concept of intelligence, as a dimension that differentiates between people, has long been used as a basis for discrimination, whether in education or employment, and more broadly for asserting differences between races. Since we humans possess brains that are some three times as large as those of our closest living relatives, the great apes, it has also widely been held that brain size itself must be an index of intellectual capacity. As long ago as 1836, the German anatomist Frederick Tiedmann wrote that there exists ‘an indisputable connection between the size of the brain and the mental energy displayed by the individual man’, and in 1839 the American physician Samuel George Morton wrote a treatise in which he compared the skulls of various racial groups, with the aim of drawing conclusions about their intellectual capacities (Morton, 1839). Not surprisingly, he declared Caucasians to have the largest brains and superior intellect, followed in turn by Asians, Native Americans and ‘Negroes’. Morton's views were widely used to justify slavery, at least until it was abolished in the USA in 1865, although racial segregation was advocated well into the 20th century. Such claims were roundly debunked by Stephen Jay Gould in his 1981 book The Mismeasure of Man , but this has not prevented claims of racial difference from rippling on. An extreme example is that of the Canadian psychologist J. Philippe Rushton, whose 1995 book Race, Evolution and Behavior purports to show systematic racial differences—although for Rushton it is East Asians, not Caucasians, who score highest, both in terms of intelligence and average brain size. Africans are …
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".