Memerangi kejahatan kehutanan dan mendorong prinsip kehati-hatian perbankan untuk mewujudkan pengelolaan hutan yang berkelanjutan: pendekatan anti pencucian uang
Bibliographic record
Abstract
If illegal logging was a crime involving only poor forest-dependent people, truck drivers or underpaid forest rangers, it would not be difficult to stop. With involvement of financiers of illegal logging, known as cukong, legal timber industries, and government officers, illegal logging becomes a complex problem not only for Indonesia, but also for the international forestry community. The current forestry law enforcement approach fails to capture the masterminds of illegal logging. However, the money laundering law enforcement approach which ‘follows the money' provides an important option to deal with the masterminds of illegal logging. This new approach requires banks and other financial service providers to be more active and prudent in dealing with financial transactions related to their customers. Bank customers could include financiers of illegal logging, timber industries, law enforcement and government officers. Overall, proper implementation of the anti money laundering regime should provide opportunities for promoting prudent banking practices and sustainable forest management, and for curtailing forestry crimes.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.013 |
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".