Անօրինական միգրացիայի կազմակերպման համար պատասխանատվությունն ըստ արտասահմանյան երկրների քրեականօրենսդրության
Bibliographic record
Abstract
The article discusses issues related to the peculiarities of legal norms provided for in the criminal legislation of foreign countries, which establish responsibility for organization of illegal migration. In this regard, an analysis of the relevant legislative acts of the member states of the Eurasian Economic Union, the European Union, as well as a number of other foreign countries (USA, People's Republic of China, UK, Canada, Australia, Islamic Republic of Iran) is carried out. In particular, it is emphasized that, unlike the countries of the Eurasian Economic Union, in most of the mentioned foreign countries, responsibility for the discussed crime is established not only by the Criminal Code, but also by other legal acts regulating certain spheres. The authors also pays special attention to the fact that the Law on Islamic Punishments of the Islamic Republic of Iran, neighboring Armenia, does not provide for legal norms establishing liability for organization of illegal migration. In this regard, it is emphasized that this country is one of those states in which the organization of illegal migration is not considered as a socially dangerous, criminal phenomenon.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".