When copulas and smoothing met: An interview with Irène Gijbels
Bibliographic record
Abstract
Since the early 1990s, Irène Gijbels has gained an international reputation for her deep and extensive contributions to the theory and applications of semi-and nonparametric statistical methods.She had briefly explored the use of smoothing techniques for copulas at the very beginning of her career.After a hiatus of nearly 20 years, she revisited copula modeling and quickly became one of the most prolific and influential researchers in the field.She has shown, among others, that smoothing is a natural paradigm on which to rely for conditional inference in this context, much like rank-based methods are in an unconditional setting.The following conversation, held virtually during the COVID-19 pandemic, gives an overview of her scientific journey.In the following, our questions to Irène are typeset in bold-face.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.010 | 0.039 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".