Bibliographic record
Abstract
David Cox was the leading statistical scientist of his generation and had an extraordinary influence on the field. His research career spanned some 75 years and 386 published works, with several drafts in preparation at the time of his death. He was held in extremely high regard by the community of scholars for his seminal contributions to scholarship, his enthusiasm for science and his generosity of intellect. One obituary called him a ‘rock star’ of the statistical world, and in spite of the hyperbole the description is apt. His work was very broad; he made influential contributions to the fields of experimental design, stochastic processes, statistical methodology, foundations of inference, statistics in medicine and public health, and more. His 23 published books continue to be key references for students and researchers. His most widely cited paper ( J. R. Stat. Soc. B 34 , 187–220 (1972)) introduced what is now called the Cox model for the analysis of survival data; this was included in Nature ’s list of the top 100 cited scientific papers of all time. He received many accolades, including the Copley Medal (2010) and the inaugural International Prize in Statistics (2016).
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.080 | 0.090 |
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".