Bibliographic record
Abstract
Abstract He was large and somewhat clumsy; he had a thundering voice, and piercing eyes that are startling even in old photographs. The conventional role of the intellectual did not appeal to him, so he played it his own way. Once, a distinguished stranger, amazed by his unscholarly accent and appearance, mistook him for an Australian farmer. (His New Zealand origins partly explain the impression.) A nonscientific academic colleague told him he was a “savage—a noble savage I admit—but still a savage!” (This response was not unprovoked. Rutherford had opened the conversation with: “Alexander, all that you have said and all that you have written during the last thirty years—what does it all amount to? Hot air! Hot air!”) He was not inclined toward modesty, if undeserved. After moving from McGill University in Montreal, where he did some of his earliest research, to the University of Manchester, which saw the middle period of his career, he reported to his friend and colleague Bertram Boltwood at Yale: “I find the students here regard a full professor as little short of Lord God Almighty. It is quite refreshing after the critical attitude of the Canadian students. It is always a good thing to feel that you are appreciated.”
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.043 | 0.007 |
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".