Bibliographic record
Abstract
Hersch Lauterpacht observed in 1952 that “if international law is, in some ways, at the vanishing point of law, the law of war is, perhaps even more conspicuously, at the vanishing point of international law.”[1] More than 70 years later, Lauterpatch’s observation overstates the indefiniteness of modern international humanitarian law – the jus in bello or law of armed conflict governing the means and methods of armed conflict. However, it remains a fair critique of the jus ad bellum – the law that addresses the “rightness” of a recourse to force. Even more pointed would be a codicil, addressing the relationship between this jus ad bellum and the right of self-determination: The vanishing point at the vanishing point of international law is the relationship between the regulation of force and the right to self-determination. At this convergence point, two challenging questions of high politics intertwine to compound the indeterminacy of the law. The result is an area fertilized with opinions but hobbled with uncertainty. This fact is not accidental. States have little incentive to lay a roadmap for their lawful forcible dismemberment. Still, it may be possible to tease from the accrual of state practice answers to some questions in this area if factual scenarios are carefully parsed.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.071 |
| Scholarly communication | 0.011 | 0.023 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".