Strategic Real Options – with the German Electric
Bibliographic record
Abstract
This dissertation emerged during the past two years when I was a scholarship holder at the Dortmund Graduate School (”Graduiertenkolleg”). The Graduiertenkolleg is a research program on Allocation Theory, Economic Policy and Collective Decisions and is financed by the Deutsche Forschungsgemeinschaft (DFG). The present thesis has been strongly influenced by frequent presentations in the Graduiertenkolleg’s workshop and numerous discussions with professors and fellow students of the Graduiertenkolleg. I am grateful to all who supported my research in this way, in particular to Professor Wolfgang Leininger, Ph.D., and Professor Dr. Walter Krämer who both supervised this dissertation. Moreover, I would like to thank Susanne Maidorn and Christian Bayer who helped to markedly improve the final version of this work. My future colleagues from RWE Trading, especially Michael Römmich, who provided a lot of practical knowledge and helpful suggestions should not remain unmentioned. I also thank the Deutsche Forschungsgemeinschaft for its financial support. Finally, I am indebted to my wife Nita and my little son Moritz for their all-out support and immense
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".