Bibliographic record
Abstract
English Abstract: The purpose of this article is to contribute to the continuing debate over the relevance of International Humanitarian Law (IHL) to cyberwar. It does so by taking what is often said to be a particularly archaic aspect of IHL, the French Revolutionary notion of levée en masse, and asking whether the concept could have relevance in the cyber context. The article treats levée en masse as a litmus test for the law’s relevance; if this IHL “relic” could have relevance in the cyber context, then the continued relevance of the larger body of rules should also be less doubtful.\nFrench Abstract: Cet article se veut une contribution au débat qui a cours sur la pertinence du droit humanitaire international (DHI) dans le contexte d’une cyber-guerre. Pour ce faire, l’auteur utilise ce qui est souvent qualifié d’aspect particulièrement archaïque du DHI, le concept français révolutionnaire de levée en masse, et demande si ce concept pourrait être pertinent dans le contexte du cyberâge. L’article traite la levée en masse comme critère décisif de la pertinence de la loi; si ce vestige du DHI peut être pertinent dans le contexte du cyberâge, alors le maintien de la pertinence de l’ensemble des lois et des règlements devrait aussi être moins douteux.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.052 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".