Contributions to mathematics, statistics, econometrics, and finance : essays in honour of professor Seppo Pynnönen
Bibliographic record
Abstract
This volume is dedicated to Seppo Pynnönen on the occasion of his 60th birthday and consists of 24 contributions by more than 40 authors in the areas of mathematics, statistics, econometrics, and finance.As a consequence of his social and academic skills, Seppo Pynnönen has as a dedicated researcher, teacher, and friend over the years interacted with many people from a wide range of academic areas.This collection provides us an opportunity to express our appreciation and gratitude to him.Seppo Pynnönen is a very friendly person who is also highly dedicated to all his professional duties such as research, teaching, and administration.Furthermore, he has an ability to stay focused and productive with a positive and inspiring attitude.He is also devoted to his family and keeps up a continuous activity with his sports interest, especially his passion for tennis.Seppo Pynnönen would not travel anywhere without his tennis gear.We are grateful to all the contributors of this volume for their willingness to submit their work, thus joining us in honoring our dear friend and colleague Seppo Pynnönen.On behalf of all the contributors we would like to say that it is a privilege to have the opportunity to know, collaborate and interact with Seppo Pynnönen and we all look forward to continuing this for many years to come.We would finally like to thank Tarja Salo for her valuable editorial help with this volume.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.017 |
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".