Social Practices of Rule-Making for International Law in the Cyber Domain
Bibliographic record
Abstract
Abstract In 2013, despite deteriorating relations between Russia and the United States and increased global contention over cybersecurity issues, participating states in the First Committee of the United Nations General Assembly agreed on a landmark report endorsing the applicability of existing international law to state military use of information technology. Given these conditions, the timing of this agreement was surprising. In this article I argue that state representatives engaged in a rule-governed social practice of applying old rules to new cases, and that the procedural rules governing this practice help to explain the existence, timing, and form of the agreement. They also help to explain further agreements expressed in a follow-on report issued in 2015. The findings of the case study presented here demonstrate that social practices of rule-making are simultaneously rule-governed and politically contested, and that outcomes of these processes have been shaped by specialized rules for making, interpreting, and applying rules. The effectiveness of procedural rules in shaping the outcome of a contentious, complex global security issue suggests that such rules are likely to matter even more in simpler cases dealing with less contentious issues.
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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 | 0.045 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.096 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".