Bibliographic record
Abstract
Abstract Without exception, great statisticians have penetrating statistical insight. Technical strength is likely secondary in their research, although it never hurts. In contrast, mathematical skill has been pivotal in my work, particularly in combination with my proximity to many great statistical minds. In the occasion of receiving the Gold Medal of the Statistical Society of Canada, I wish to attribute part of my success to the joy of proofs. Proofs are an indispensable ingredient in my research achievements. I hasten to add that proofs are delightful only if they help to develop innovative statistical methods: proving theorems solely for the sake of publication can be boring and a waste of time. With this preamble, I selectively present some research achievements and encourage other researchers to proudly maximize the benefits of their technical strength. The Canadian Journal of Statistics 43: 481–497; 2015 © 2015 Statistical Society of Canada
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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 | 0.121 | 0.394 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".