Cross-Border Evidence Gathering in Transnational Criminal Investigation: Is the <i>Microsoft Ireland</i> Case the “Next Frontier”?
Bibliographic record
Abstract
Abstract A recent and prominent American appeals court case has revived a controversial international law question: can a state compel a person on its territory to obtain and produce material that the person owns or controls, but which is stored on the territory of a foreign state? The case involved, United States v Microsoft, features electronic data stored offshore that was sought in the context of a criminal prosecution. It highlights the current legal complexity surrounding the cross-border gathering of electronic evidence, which has produced friction and divergent state practice. The author here contends that the problems involved are best understood — and potentially resolved — via an examination through the lens of the public international law of jurisdiction and, specifically, the prohibition of extraterritorial enforcement jurisdiction. An analysis of state practice reveals that unsanctioned cross-border evidence gathering is viewed by states as an intrusion on territorial sovereignty, engaging the prohibition, and that this view properly extends to the kind of state activity dealt with in the Microsoft Ireland case.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".