EKSTRADISI MENG WANZHOU DALAM PERSPEKTIF HUKUM INTERNASIONAL
Bibliographic record
Abstract
Extradition is one of the international law field of studies. One of that controversy is Meng Wanzhou’s case, chief financial officer of Huawei. She is known as the daughter of Huawei’s founder Ren Zhengfei and holding Chinese citizenship. Meng Wanzhou was captured and detained by Canadian authority by the request of United States of America when she transitted Canada at Vancouver Airport December 2018. She was charged of frauding which is related to Skycom, a technology company based in Iran. This event led the diplomatic ralation between China, Canada and United States excalated. China government had already released an official statement which expressed anger and their objection about the charged and detention. On the other side, Canada and United States insisted in this event genuinely only a legal matter. This research conducted by a normative and IRAC methods for the analysis part. Based on the research, the request of extradition by United States was consistent with international law principle. Every objection matter by any Party of this case should be done by every diplomatic channels. This thing should be done to maintain the world’s peace.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".