Bibliographic record
Abstract
There has been formed many viewpoints in criminal law world regarding the issue of how to distinguish omission criminal made and accomplice; in general, however, they can be classified into three categories of principle criminal made theory, principle accomplice theory, and compromise theory. Although these three types of viewpoints have both advantages and drawbacks as well; on the whole, they are unable to propose satisfactory answers on how to differentiate omission criminal made and accomplice. In view of this, author, with the hope to settle this issue, present his unique opinion from Pflichtdelikt and dominated committed perspective. Keywords: omission; criminal made; accomplice; dominated committed; PflichtdeliktResume: Afin de faire la question de differenciation avec le complice d'inaction, des cercles de loi de châtiments ont forme un grand nombre de vues a ce sujet. Mais on peut diviser ces vues en trois categories: le principe qui fait, le principe qui permet de faire et le principe qui compromet. Ces trois types de vue ont leurs avantages et leurs defauts. Pourtant, ces trois types de vue ne peuvent pas donner une reponse satisfaisante pour distinguer le complice d'inaction. Pour cette raison, dans l'espoir de resoudre ce probleme, je voudrais faire l'introduction, l'evaluation et l'analyse de ces trois types de vues, et proposer mon propre point de vue de faire et de controler l'angle qui est fait volontairement.Mots-cles: omission, criminel, complice, devoir delit commis domine
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".