Bibliographic record
Abstract
A specious clarity can be more damaging than an open-ended vagueness Lon Fuller, The Morality of Law (1969) Even though norms might have an open texture, they nevertheless possess a core meaning and a penumbra which will not accommodate any and all possible applications. Some characterisations will have to be considered unreasonable in order to uphold the normative nature of a rule. Clearly, the concepts of reasonableness and good faith will set parameters, even if ill-defined, restricting the ambit of legitimate characterisations. Indeterminate norms, however, will accommodate a diversity of characterisations which cannot be labelled unreasonable or in bad faith. It is within that sphere of legitimate diversity that the thorniest problems of characterisation arise. It is therefore important, before passing on to the study of the relative effect of characterisation, to examine the degree of indeterminacy of the relevant rules. The overview of applicable standards centres first on the various categories of armed conflicts and their particular legal regimes, and secondly on the elements which have been found to lawfully justify derogation from human rights norms during a state of emergency. Humanitarian law of armed conflict Five different types of situations are differentiated under the humanitarian law of armed conflict, the last one of which, internal disturbances and tensions, falls outside the reach of that law. The four others, which will be examined successively, consist of (1) inter-state armed conflicts, (2) national liberation armed conflicts, (3) non-international armed conflicts as defined under the 1977 Additional Protocol II, and (4) internal armed conflicts as defined under common Article 3 of the 1949 Geneva Conventions.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 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".