Kai kurie vagystės kriminalizavimo probleminiai aspektai Lietuvos ir kitų užsienio šalių baudžiamuosiuose įstatymuose
Bibliographic record
Abstract
Article is aimed to analyze theft, as it is foreseen in Article 178 (theft) of The Criminal Code of the Republic of Lithuania. The aimof this study is to reveal the advantages and disadvantages of criminalizing theft in Lithuanian criminal law and to submit proposals to the legislator regarding adding and amending features of the act. In order to achieve these goals, the following tasks are set: to provide a historical overview of the criminalization of theft, to perform a detailed analysis of the criminalization of theft in the criminal laws of Lithuania and other countries, to submit proposals to the legislator of the Republic of Lithuania regarding the establishment of a certain subjective features of theft, to find out whether it is necessary to expand the subject matter of the theftinLithuanian criminal law. While trying to evaluate the current legal regulation established in the CC of Lithuania, at the same time the authors examine the criminal laws of other countries (Germany, Poland, Hungary, France, etc.)In their article, the authors also examine the identification of objective and subjective features in criminalizing theft. The authors point out that, for example, criminalization of theft in criminal law, in some countries, including Lithuania, name propertyof another(Latvia, Estonia, Poland, Russia, Italy, Australia, England) as theft, others a objectof another(Germany, Hungary, France, Turkey), and still others (Canada and England) use a broader term to define the subject of theft -money, any other movable and immovable property, as well as the right of claim and other intangible property. Moreover, the authors point out that subjective features (motive and purpose) in criminalizing theft are not enshrined in Article 178 of the CC of Lithuania. Meanwhile, the criminal laws of some foreign countries also define theft as a sign of selfishness -seeking to obtain(material) benefits [...].
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".