Bibliographic record
Abstract
The characterisation of situations as armed conflicts under humanitarian law and as states of emergency under human rights law raises, at a basic level, the same problem: how to assess the legal character of these situations in the presence of characterisations which are often numerous, usually contradictory, and sometimes legally unfounded? The difficulty is one that affects international law as a whole, leading states to rely on their own appreciation of whether there has been a material breach of a treaty before suspending it, whether a norm has been violated before adopting countermeasures, or whether there has been an armed attack before acting in self-defence. That being said, the fact that indeterminacy affects all of international law does not mean that a universal solution can be found. As this entire book has sought to show, different areas of international law will have distinct normative dynamics, calling for a modulated response to the challenge of indeterminacy. Thus a number of significant differences between characterisation of situations under human rights and humanitarian law have emerged in the course of the preceding analysis. These differences stem from both the context of application and the nature of norms in human rights and humanitarian law. First, characterisation of a situation as an armed conflict necessarily implies the possibility of competing autoqualifications by the various belligerents. Even in the case of an internal conflict, international law grants insurgents a measure of functional sovereignty whereby they are entitled to make a valid legal characterisation of the conflict.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.106 | 0.033 |
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".