Bibliographic record
Abstract
Introduction Michael K. Ong (The Stuart Graduate School of Business, Illinois Institute of Technology) Part 1 - Managing Capital 1. Maximising Capital Efficiency Through Active Credit Portfolio Management Alistair McLeod (Barclays Capital) 2. The Interplay Between Risk and Return Bogie Ozdemir and Peter Miu (Sun Life Financial Group (BO) and DeGroote School of Business McMaster University (PM)) 3. Assessing the Emerging Caveats of the New Market Risk Metrics Peter Dobranszky (BNP Paribas and Katholieke Universiteit Leuven) 4. Fixing What's Broken in Market Risk Capital Models Michael Gibson (Federal Reserve Board) 5. Beware of the Tail: A Survival Guide to the Tail Risk Wilderness Evgueni Ivantsov (HSBC) 6. Foreign Reserve Management & the Global Financial Crisis Orhan Kandar (Central Bank of the Republic of Turkey) 7. Managing Liquidity Risk in a New Funding Environment Peter Neu and Pascal Vogt (The Boston consulting Group) 8. Portfolio Construction and Risk Management under Non-normality Ho Ho (MSCI Inc) 9. A Roadmap to Connect Capital, Return and Credit Risk Strategies Danny Dieleman and Tamar Joulia-Paris (ING Bank (DD) and St Louis University (T J-P)) Part 2 - Measuring Capital 10. Economic and Regulatory Capital for Counterparty Credit Risk Michael Pykhtin (Federal Reserve Board) 11. Bilateral Credit Valuation Adjustment with Application to Credit Default Swaps Damiano Brigo and Agostino Capponi (King's College London (DB) and Purdue University (AC)) 12. Model-based Downturn PDs for Basel III Esa Jokivuolle and Matti Viren (Bank of Finland) 13. Capital Allocation for Credit Portfolios under Normal and Stressed Market Conditions Dirk Tasche and Norbert Jobst (FSA, London(DT) and Lloyds Bank (NJ)) 14. Designing an Effective ICAAP as an Integrated Capital Planning Tool: Managing Capital Adequacy and Procylicality Bogie Ozdemir and Peter Miu (Sun Life Financial Group (BO) and DeGroote School of Business McMaster University (PM)) 15. Addressing Procyclicality: Credit Cycles, Stress Scenarios and Portfolio Losses Jorge Sobehart and Rodolfo Giacone (Citigroup) 16. Fully Integrated Capital Models and Economic Scenario Generation Alexander McNeil, Gavin Kretzschmar and Axel Kirchner (Maxwell Institute for the Mathematical Sciences, Edinburgh (AM), EADA Business School Barcelona (GK) and Barrie & Hibbert Limited (AK)) 17. Measuring the Performance of a Business Within a Bank Stuart Turnbull (Bauer College of Business, University of Houston) 18. Risk-adjusted Performance Measurement under Model Risk Francesco Saita (Bocconi University)
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 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".