Bibliographic record
Abstract
Philip Ewart Vernon was a British psychologist who studied intelligence, environment, and creativity. He was born in 1905 in Oxford, England, where his father was a lecturer in physiology at the University of Oxford. At Cambridge University Vernon studied classics and natural sciences before he seriously focused on psychology. He received a B.A. with class honors, and in 1927 completed the Ph.D. He went on to complete two postdoctoral research fellowships, one at Harvard University and the other at Yale University. Vernon then returned to the U.K., where he held a number of significant positions for the following 35 years. He was a teaching and research fellow at Cambridge and later, in 1939, was appointed head of the psychology department at the University of Glasgow. In 1952, he received a D.Sc. degree from the University of London, where he also served as professor of educational psychology (appointed in 1964). However, in 1968, and after a successful career, Vernon moved to Canada where he became a distinguished professor at the University of Calgary. There he embraced the Canadian lifestyle, remained fairly involved on the international level, and eventually received Canadian citizenship. In 1978, he received an honorary Doctor of Laws degree from the University of Calgary.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.214 | 0.085 |
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".