Politics behind the law: unveiling the discursive strategies in extradition hearings on Meng Wanzhou
Bibliographic record
Abstract
Abstract Deciphering the hidden political implications in legal discourse has become hot foci in the study of international politics to unravel the political roles and positionings of various stakeholders in law as well as its enforcement and adjudication. Drawing on CDA approach, this study provides a text mining of 12 extradition hearings on Meng Wanzhou case. The findings of the present study indicate that the case is in the name of law but actually with the nature of politics in the context of the U.S.–China trade war. It also demonstrates evidence of manipulation of political power and reframing of the event occurrences throughout the texts of the 12 hearings, by exerting the repetitive use of a bundle of legal discursive strategies. The violation of justice and equality in the legal discourse around the present case is based on the superior status of the U.S. in contrast with Canada in the discursive practices as well as the political contemplation of Canada, resulting in challenges to the fundamental principles of rule of law around the world. This research furthers the understanding of the strategies and entanglement of justice and injustice, power and control in the process of discourse construction.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".