Bibliographic record
Abstract
Abstract Nancy Reid was born in September 1952 in Niagara Falls, Canada. She graduated from the University of Waterloo with a Bachelor in Mathematics and a Major in Statistics in 1974. She studied statistics at the University of British Columbia (UBC) where she obtained a Master's in Applied Mathematics in 1976, and at Stanford University where she graduated with a PhD in Statistics in 1979. After spending one year at Imperial College London visiting Sir David Cox, she joined UBC as an Assistant Professor in the Department of Mathematics, and in 1986 she moved to the University of Toronto as a faculty member in the Department of Statistics (now Statistical Sciences) where she has been ever since including serving as Chair between 1997 and 2002. At the time of writing, Nancy has authored over 100 papers and 5 books, including seminal developments in conditional inference, higher‐order asymptotics, composite likelihood, and Bayesian inference. Her outstanding contributions to statistics have been recognized nationally and internationally with many awards, including the President's Award of the Committee of Presidents of Statistical Societies (COPSS), the Gold Medal awarded by the Statistical Society of Canada (SSC), and being elected Foreign Associate of the National Academy of Sciences. In 2017, the International Statistical Review published Nancy's conversation with Ana Maria Staicu [Staicu, A. M. (2017). Interview with Nancy Reid. International Statistical Review, 85(3), 381‐403.], which had a biographical emphasis. Since then, Nancy has continued to support the discipline of statistics in important ways, such as by serving as Director of the Canadian Statistical Sciences Institute (CANSSI) (2015–2019) and Co‐chair of the Institute of Mathematical Statistics' Committee on Ethics (2018–2020). Her research activity continues to be celebrated with important awards such as Fellowship of the Royal Society of London (2018), the inaugural Hollander Distinguished Lectureship at Florida State University (2020), the Distinguished Achievement Award (and Lectureship) from COPSS (2022), and the Guy Medal in Gold from the Royal Statistical Society (2022). In May 2022, the Department of Statistical Sciences at the University of Toronto, in collaboration with CANSSI and the SSC, organized a one‐day conference, “Statistics at Its Best”, in honour of Nancy's 70th birthday. This conversation took place in Toronto around the time of the event. Its focus is on Nancy's views on building a career in statistics, and the challenges and opportunities statisticians encounter within the rapidly evolving data science ecosystem.
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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 | 0.025 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 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".