Bibliographic record
Abstract
Extract This book is a revised and updated version of a PhD thesis I defended at KU Leuven in September 2019. I owe a tremendous amount of gratitude to my PhD supervisor, Professor Geert De Baere; my co-supervisor, Professor Stefan Sottiaux; the members of my PhD supervisory committee Professors Koen Lenaerts and Daniel Halberstam; and the members of my PhD defence committee Dr Kathleen Gutman and Professors Robert Schütze and Gleider Hernández. The initial PhD research was generously funded by Research Foundation—Flanders (FWO). Thank you to Professor Geert De Baere and Dr Tina Van den Sanden for their efforts in obtaining the initial FWO grant. Writing the PhD and the ensuing book would not have been possible without the financial support of the Belgian American Educational Foundation and the hospitality extended to me by the entire academic community at KU Leuven’s Law Faculty, by Professor Daniel Halberstam at the University of Michigan Law School, by Professor Stéphane Beaulac at the Université de Montréal, and by Professor Koen Lenaerts at the Court of Justice of the EU.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.018 |
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; both teacher heads 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".