Discussion on the Problem of Misunderstanding in Mittelbare Täterschaft
Bibliographic record
Abstract
The problem of misunderstanding in mittelbare taterschaft (indirect offence) causes a big controversy in the identification of the indirect offenders. Plenty of researches have been done around this issue by scholars, and many different views have been brought up. Among them, the focus of controversy is mainly about two particular situations: the user having a wrong knowledge about the nature of the tool being used, and the person used becoming an insider halfway through the crime. Through the analysis of existing representative principles and perspectives, the authors believe the user should be considered as constituting indirect offence if the user’s practice is equivalent to an instigator, with the mean of an indirect offender, due to misunderstanding; similarly, the user should also be punished as an indirect offender if the user’s practice is equivalent to an indirect offender, with the mean of an instigator, due to misunderstanding; on the other hand, punish the user as an indirect offender under the circumstance when the innocent person used becomes an insider seems reasonable. By discussing the problem of misunderstanding in indirect offence, we hope we can benefit from it for further research on problems relevant to indirect offence.
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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 | 0.030 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.045 |
| Scholarly communication | 0.013 | 0.028 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.018 | 0.016 |
| 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".