Bibliographic record
Abstract
First and foremost, I would like to thank Klaus M. Schmidt. He has been the best supervisor and mentor one could possibly imagine- offering invaluable guiding advice and inspiring encouragement. His support was truly exceptional. I am also much indebted to Florian Englmaier, Fabian Herweg, and Markus Reisinger. Florian Englmaier and Fabian Herweg kindly agreed to serve as second and third re-viewer on my dissertation committee, and with Florian Englmaier and Markus Reisinger I was writing the second and third chapter of this dissertation. They provided a great source of advice and support and it was always very joyful to work with them. I was very fortunate to spent one semester as Research Fellow at Columbia Univer-sity. I thank C. Scott Hemphill for supervising me during this time, giving me guiding advice from a legal perspective. I also thank Annette Kur and Klaus M. Schmidt for supporting me with my applications and the DAAD for financing the research stay. I very much appreciate insightful discussions with friends, colleagues, and participants at conferences and seminars. Bernhard Ganglmaier and Johannes Maier deserve special mention for their invaluable advices and suggestions.
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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 | 0.064 |
| 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 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.275 | 0.199 |
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".