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 Jim Ramsay was born on September 5, 1942, in Prince George, British Columbia. He pursued undergraduate studies at the University of Alberta, where he completed a BEd in 1964 with a major in English and a minor in mathematics. He then specialized in statistics and psychometry, earning a PhD in psychology from Princeton University in 1966. After holding a temporary lectureship in the Department of Psychology at University College London for one year, he joined the Department of Psychology at McGill University, where he rose through the academic ranks. He was chair of his department from 1986 to 1989 and spent sabbatical leaves in Cambridge, Grenoble, and Toulouse. He was named professor emeritus upon his retirement in 2007. Jim is the author of four influential books and over 100 peer‐reviewed articles in statistical and psychometric journals. He developed much of the statistical theory behind multidimensional scaling and is widely recognized as the founder of functional data analysis. Three of his papers were read to the Royal Statistical Society, and another won The Canadian Journal of Statistics 2000 Best Paper Award. The Statistical Society of Canada (SSC) awarded him a Gold Medal for research in 1998 and an honorary membership in 2012. Jim was president of the Psychometric Society in 1981–82 and president of the SSC in 2002–03. The following conversation took place at Jim's home in Ottawa, Ontario, on March 14 and April 4, 2012.
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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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