Bibliographic record
Abstract
There are always probabilities of strong relationships between transnational organized crime group, the government, semi-government agencies and irregular formations. Because, at the fundamental level, motivations and aspirations of all these agencies and groups may be similar, i.e. making as much money or profit as quickly as possible; whether in a semi-legitimate or illegitimate means and ways. For this, they will be ready to use any modus operandi; the end result and harm they cause to the nation and society will be the same. Therefore, more often it is also very difficult to distinguish them one from another. In this regard, they all can be termed “the silent partners” of the legitimate government agencies, semi-government corporations and the organized crime groups (OCGs) and wherever they belong: they converge at a single platform, i.e. the Organized Crime. Therefore, the silent partners of the Organized Crime Group can be any of these: a private party or person, a government officer or his office, a semi-government official or a group of people belonging to these organizations. There may be another similarity among these too;…i.e. they all do their utmost to avoid their appearance in public or willingly acknowledged their involvement in any form and deeds on such cooperative undertaking. There are many ways such organized syndicates apply their methods that may be soft, peaceful to even gruesomely violent means to get access to state power, money or government resources. In this regard, they may apply all types of legitimate and illegitimate means, to name the few, such as protection rackets, and capture public resources, seize of property and land forcibly in an illegitimate way, and eventually entry into the licit private sector by money laundering and other means.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".