Bibliographic record
Abstract
This is the author’s response to the admirable contributions in a symposium on my book, Justice in Extreme Cases: Criminal Law Theory Meets International Criminal Law. The symposium was published in the Temple International & Comparative Law Journal. In response to questions, I clarify some of the arguments in the book. One area of debate was how we resolve ambiguities in fundamental principles. I argue that we do not mechanically deduce the answers from a master theory; instead we draw on a web of normative clues to flesh out the principles – a “coherentist” method. Recognizing the underlying method allows for more rigour, transparency, and humility about our conclusions. Other questions relate to command responsibility. In my book, I unpack the debate over command responsibility, in order to demonstrate how early ICL failed to engage in deontic reasoning, and how the resulting contradictions generated so much confusion and controversy today. In this response I clarify the scope and purpose of that case study.
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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.025 | 0.021 |
| Insufficient payload (model declined to judge) | 0.018 | 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".