Teaching an Old Law New Tricks: International Environmental Law Lessons for Cyberspace Governance
Bibliographic record
Abstract
This article uses international environmental law as a lens for analysing States’ obligations in relation to cyber activities of non-State actors operating under their jurisdiction. We begin by exploring opportunities for borrowing well-established rules of harm prevention and due diligence from the more advanced, but not dissimilar, field of international environmental law. We argue that these rules can provide a legal foundation for the emerging field of international cyber law, and have already made their way into the cyberspace discourse. We then draw on the experience of international environmental law with conceptual notions such as ‘global commons’ and ‘shared resources’, as well as with institutional models such as multilateral environmental agreements and norm-developing bodies. We highlight the risks and drawbacks of the conceptual and institutional leap from States’ transboundary harm prevention duties to protection of a commons in the ‘virtual’ world of cyberspace.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".