Bør civile data anses for beskyttede "objekter" under den humanitære folkeret?
Bibliographic record
Abstract
International Humanitarian Law (IHL) aims to limit the effects of armed conflict. It divides the world into legitimate military objectives that may be lawfully targeted during armed conflict, and protected civil ian persons and objects that are to be protected from attack. IHL provides clear guidance on how to distinguish one category from the other – at least in the physical realm. The relatively new domain of cyberspace, however, is characterized by a number of features unknown at the time of IHL’s foundation. As a consequence, there is currently no consensus on whether and how the rules on targeting apply to digital data. However, recent examples from the war in Ukraine have shown that cyberspace and attacks on data are part of modern armed conflicts, and the question of the legal status of data in IHL is therefore highly topical. On that basis, this report investigates whether data should be understood as protected civilian objects through an analysis of the legal debate on the matter and statements made by states in this regard. The report identifies two major positions in the legal debate on the status of data: The traditionalist school, which emphasizes the fact that ‘an object’ has traditionally been defined as something visible and tangible, and that data therefore should not be considered an object, as the protection provided by IHL would otherwise extend beyond its intended scope; and the progressive school, which emphasizes that the purpose of IHL is to protect civilians from the effects of armed conflict, and that civilian data should therefore be considered an object, as the protections provided by IHL would otherwise be more restricted than intended. Both schools posit valid legal interpretations but arrive at polar opposite conclusions. This report therefore suggests a contextual interpretation as a supplement to the two schools: As the rules and legal structure of relevant IHL regulation intend to divide the world into objects that are either legitimate military objectives or protected civilian objects, it cannot have been the intention for anything directly targetable in an armed conflict to fall outside the scope of the rules. Through this approach, data should be considered an object and civilian data protected civilian objects. As there is not yet a clear picture of which interpretation states generally support, the report goes on to review government statements and other written materials from states that have expressed opinions on the matter and compare them with official Danish statements on the ques tion. The report finds Denmark to be one of only three states maintaining a traditionalist interpretation of the question, while significantly more states – including several of Denmark’s close allies – support the progressive approach. Largest, however, is the group of states that either leave the question open or have yet to comment on it. Based on the analysis of the legal debate and state positions, the report concludes that the interests of Denmark are best served through the adoption of an open position, where technological advancements can give rise to new considerations on the matter, and through contributing to a clarification of the legal concept of ‘military operations’, which might shed light on aspects of the protection of civilian data under IHL, regardless of the outcome of the current debate on data as an object.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.025 |
| Scholarly communication | 0.033 | 0.033 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.028 | 0.013 |
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".