Transplanting Legal Context without the Law: Double Criminality in Meng Wanzhou’s Extradition Case
Bibliographic record
Abstract
Law is often transplanted from one place to another. Law is inextricably linked to its social context. In the new location, it will operate differently because the legal context changes. The law undergoes a transformation. In its new context, it might fulfill its intended purpose or satisfy a different one. In the extradition case of Meng Wanzhou, CFO of the Chinese private company, Huawei, the Canadian Court applied the double criminality test. This involved transferring the social context of the requesting jurisdiction, as it attached to the alleged conduct constituting the offence, to judge whether the domestic offence requirements could be satisfied. The social context may include background law comprising the sociolegal landscape. However, the law creating the offence is not transplanted for this purpose, as only the local law is relevant, not that of the foreign jurisdiction. This article reviews the application of the test and questions whether the deciding Court went too far by using foreign law, that creating US Sanctions against Iran, which does not exist in Canada, to satisfy a required element of the local offence. The article posits that the legal element of the transplanted "context" shifted from passive background context to playing a performative role in the Court"s decision that the double criminality test had been satisfied. It is suggested that further study of the previous work of legal comparatists might help identify the role of transplanting law and context in this aspect of the extradition process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".