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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
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".