A teoria da trilidade como relevante ferramenta na interpretação e subsunção da norma jurídica de ordem pública
Bibliographic record
Abstract
One of the great challenges in the legal sphere, especially for those without legal training, is the ability to interpret legal rules by correctly subsuming their content to specific cases. This challenge arises because the normative field often relies on dualistic thinking: right or wrong. In many situations, particularly when dealing with public order rules, this binary approach can limit the interpreter's actions, who becomes conditioned by the relevant law's strictures. Thus, this study aims to demonstrate that other possibilities for interpreting legal norms may exist. The so-called “Theory of Trility and Human Reasoning” offers a significant contribution in this regard, emerging as a new way of approaching legal interpretation. The study investigates the extent to which these alternative interpretive methods can be beneficial in the legal field, particularly when we abandon the old habit of viewing reality through a dualistic lens. To achieve this objective, the study employed a review of specialized literature focused on the study of legal norms, their interpretation, and dialogue with scholars specializing in the Theory of Trility.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.006 | 0.058 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| 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".