Special Issue in Honour of Stephen J. Taylor: Guest Editors' Introduction
Bibliographic record
Abstract
Lecturer in Finance , Reader in Finance and Professor of Finance (1993-2020) at Lancaster.Stephen is a key authority in the area of Time Series Econometrics, especially regarding Stochastic Volatility and Option Pricing modelling.He has published more than 60 papers in the broader areas of Finance and Econometrics including in top journals such as the Journal of Econometrics, Journal of Financial & Quantitative Analysis and Journal of Financial Econometrics.Stephen has been cited extensively with more than 14,000 google-scholar citations as of 2025, and he has contributed to the careers of over 20 PhD students and numerous co-authors.Stephen was one of the very first contributors to the European Finance Association and a founding member of the Society of Financial Econometrics.His work has inspired generations of scholars in the area, and he is referenced in the Engle and Granger 2003 Nobel Prize review.His Taylor (1982) paper, introducing stochastic volatility models, is arguably his most prominent work, and it has been re-printed three times.Stephen's influential books Modelling Financial Time Series (1986) and Asset Pric Dynamics, Volatility and Prediction (2011)
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.101 | 0.092 |
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".