Bibliographic record
Abstract
My first and foremost thanks go to my supervisors, Andreas Haufler and Michael Pflüger, for their continuous stream of ideas, attention, critical comments and encouragement. Andreas Haufler always had his door, and above all mind, open for all the major and minor questions that arose, which proved extremely helpful to me. Michael Pflüger not only gave me many valuable comments on my different research projects on workshops; he also always took the time to help me on the phone when I needed advice. I am indebted to Hyun-Ju Koh and Johannes Rincke, for a very stimulating exchange of ideas and the time and effort they invested in our joint projects, which I enjoyed and from which I learned a lot. During my work on this thesis, I benefitted from many fruitful conversations with fellow graduate students at the Munich Graduate School of Economics and the Bavarian Graduate Program in Economics, which I also thank for financial and logistic support. Further thanks go to Christoph Luelfesmann (who rendered a nice and productive stay at Simon Fraser University, BC, Canada, possible) and to the other colleagues at the Seminar for Economic Policy for the many little and larger helps and the nice atmosphere. Also, I am grateful to Martin Kocher for being the third member of my thesis committee. Many, many thanks go to Kathrin Kolb for continuous encouragement and a lot of different perspectives on all the things that were important to me in this phase of my life. I am forever indebted to my family who encouraged and supported me throughout all the years of my education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.468 | 0.322 |
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".