The Current State of Transnational Organized Crime
Bibliographic record
Abstract
The paper indicates that criminal groups may differ in terms of size, the scope of operations, types of activities, territorial range, relations with authorities and management, internal organization, and the set of means and methods used to promote their criminal activities and to guard against measures taken by the Government and law enforcement bodies. When national, historical, and cultural differences are taken into account, the diversity of criminal organizations becomes even more evident, while prevention and suppression pose even greater challenges for law enforcement agencies, especially in countries where these organizations are active in their illegal activities. The study is aimed at analyzing national-level legislative measures that may be efficient about one type of organized criminal group, but may not be appropriate for other types. To solve the problems formulated, a complex methodology will be required, combining empirical studies of specific facts and a fundamental theoretical understanding of the conceptual foundations of the problem posed. The results of the analysis made it possible to theoretically substantiate the increased social danger of transnational organized criminal groups, which entails the adoption of appropriate legislative measures. Identifying the latency level of transnational crimes will make it possible to assess the efficiency of the existing sets of measures against it, as well as to formulate recommendations on improving the system of legislative measures to prevent the activities of transnational criminal organizations. A gap in the study can be attributed to the insufficient amount of statistical information on the analyzed crime. The novelty of the research lies in the comprehensive analysis of the current state of transnational organized crime.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".