Bibliographic record
Abstract
First I want to thank my supervisor Rainald Borck for his academic guidance and encour-agement. He gave me the freedom to pursue my research interests and was always willing to discuss new ideas and thoughts with me. His comments and suggestions improved every paper of the dissertation. I am also grateful to my second supervisor Michael Pflüger. His fascination for regional and urban economics spilled over and became my favorite research interest. I am also thankful for his efforts to arrange a research stay abroad. I thank Andreas Haufler who completes my thesis committee for valuable comments on my research. I am also indebted to Gilles Duranton who served as a local supervisor during my stay at the University of Toronto. I much enjoyed doing research in such an inspiring environment. I gratefully acknowledge financial and academic support from the Bavarian Graduate Program in Economics and the Elitenetzwerk Bayern. Special thanks go to my co-authors for the rewarding experience that doing research together is much more fun. Part of the chapters were presented at conferences: the meeting of the Austrian Economic
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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 | 0.008 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.356 | 0.229 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".