<i>Ius puniendi</i> and Constitution: A Comparative (Canadian-German) Perspective
Bibliographic record
Abstract
Abstract The paper inquires, from a comparative (Canadian-German) and human rights perspective, whether the State’s right (or even obligation) to punish can be derived from the Constitution. It argues that Constitutions usually assume this right but do not explicitly provide, let alone explain it (infra 1). However, protective (affirmative) duties may be derived from the rights part of a constitution (2) and these protective duties may serve as a basis for criminalization (3). While this is the position of the case law (especially the German one) and finds support in human rights law (4), it is argued that the reasoning is not fully convincing (5.1) and therefore further reflections are needed (5). First, it is necessary to make explicit the basic assumptions on the role of constitutions and judges on which the acceptance of a (constitutional) ius puniendi is predicated (5.1). Then, in a second step, the combination of a victim-based and effective remedy reasoning which best supports an obligation or at least ius puniendi is, relying on the German discussion, to be elaborated further (5.2).
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".