Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary 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 pursued her training in 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 in London visiting Sir David Cox, she joined UBC as an Assistant Professor in the Department of Mathematics, where she had an important role in the creation of the Department of Statistics. In 1986, she moved to the University of Toronto, where she has been since then as a faculty in the Department of Statistics. Nancy has served as Chair of the Department between 1997 and 2002. Nancy's research in conditional inference, higher order asymptotics and composite likelihood has been influential in Statistics. Her outstanding contributions to Statistics were recognized nationally and internationally with many awards, including the President's Award of the Committee of Presidents of Statistical Societies (COPSS), Gold Medal awarded by the Statistical Society of Canada and Elected Foreign Associate of the National Academy of Sciences. She received the Doctor of Mathematics, Honoris Causa, University of Waterloo. Nancy served with distinction as Editor of the Canadian Journal of Statistics and President of the Statistical Society of Canada and President of the Institute of Mathematical Statistics. In 2014, she was appointed as Officer of the Order of Canada for her outstanding achievements, exemplary leadership and service to Canadians. The following conversation took place at the JSM 2016 in Chicago, on August 2 and 3, 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it