Uzasadnianie twierdzeń interpretacyjnych z perspektywy derywacyjnej koncepcji wykładni prawa
Bibliographic record
Abstract
The duty to provide justification for claims is the realization of the postulate of criticism in the academia. Decisions regarding legal interpretation (regardless of whether they are formulated in the process of applying the law or outside this process) are made in the form of claims about a binding legal norm with a certain content. Validation of such claims requires their justification, just like in the academia. And in the academia, a claim remains theoretical until someone presents an inter-subjectively verified justification for it. The aim of this article is precisely that: to indicate the necessity to justify interpretive claims (both partial and final) in the process of interpretation. As a starting point in the process of demonstrating the need to formulate such a justification, we choose the derivative conception of legal interpretation, which provides for the duty to justify interpretive claims for methodological reasons (and which is an intrinsic element of the content of legal directives in this conception, at least implicitly). We present and discuss the types of justifications for interpretive claims and their variants (weak and strong). Furthermore, we analyse the difference between justifying ordinary and interpretive claims. Finally, we also identify the consequences of failure to provide such justifications.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".