Bibliographic record
Abstract
The followings are required to establish uniform principle of criminal jurisdiction for international cyber crime into customary international law; (1) clear guideline of UN for promoting national practice (2) formation of general practices based on these guidelines (3) these general practices should obtain legal confidence. International society is in close cooperation for investigating and controlling cyber threat. The US FBI has closed down the largest online crime space called 'Darkcode' and prosecuted related hackers based on joint investigation with 19 countries including England, Australia, Canada, Bosnia, Croatia, Israel, and Rumania. More and more people in Korea are raising their voices for joining cyber crime treaty, 'Budapest Treaty.' Budapest Treaty is the first international treaty prosecuting cyber crime by setting out detailed regulations on internet criminal act. Member countries have installed hotline for cyber crime and they act together. Except European countries, America, Canada, and Japan have joined the treaty. In case of Korea, from few years before, it is reviewing joining with Ministry of Foreign affairs, Ministry of Justice and the National Police but haven't made any conclusion. Different from offline crime, cyber crime is planned in advance and happens regardless of border. Therefore, international cooperation based on position of punishing criminals and international standards. Joining of Budapest international cyber crime treaty shall be done as soon as possible for enhancing national competence.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".