Modelling Shared Assets in Indonesia’s Forfeiture Bill: International Collaboration and Digital Networks
Bibliographic record
Abstract
Article 54, paragraph 3, of the UN Convention Against Corruption (UNCAC) encourages countries to implement efforts to confiscate assets resulting from crimes committed without a criminal conviction, often known as in rem. Indonesia is one of the countries that ratified the UNCAC with Law No. 7 of 2006. Further implementation of in rem forfeiture is outlined in the Asset Forfeiture Bill, which regulates the mechanism for in rem forfeiture of assets in detail. The bill also regulates asset sharing, previously only accommodated by Article 57 of Law No. 1 of 2006 concerning Mutual Assistance. Aside from being a solution to overcoming the cost of forfeiture, which tends to be large, asset sharing is also intended to prevent the interference of other forces that cause the forfeiture process not to run effectively. This mechanism also precludes different parties from sharing burdens and benefits (a win-win solution). Asset sharing is practiced in some countries, such as the United States, Switzerland, and Canada.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".