Stat Trek. An interview with Christian Genest
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
Christian Genest is Professor and Canada Research Chair in Stochastic Dependence Modeling at McGill University, Montréal, Canada. He studied mathematics and statistics at the Université du Québec à Chicoutimi (BSpSc, 1974), the Université deMontréal (MSc, 1978), and The University of British Columbia (PhD, 1983). Before joining McGill in 2010, he held academic posts at Carnegie Mellon University (1983–84), the University of Waterloo (1984–87), and Université Laval (1987–2010). Over the years, he also held visiting positions in Belgium, France, Germany, and Switzerland. Christian’s primary research focus lies in multivariate analysis, nonparametric statistics, and extreme-value theory. He also collaborates regularly with researchers in insurance, finance, and hydrology. He has published extensively and earned various distinctions for his seminal and widely cited work in dependence modeling. In particular, he received the Statistical Society of Canada Gold Medal for Research in 2011 and was elected a Fellow of the Royal Society of Canada in 2015. He has also served the profession in various capacities, e.g., as Director of the Institut des sciences mathématiques du Québec, President of the Statistical Society of Canada, and Editor-in- Chief of The Canadian Journal of Statistics (1998–2000). He is the current Editor-in-Chief of the Journal of Multivariate Analysis.
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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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