A mentira que invalida o consentimento sexual: limites à criminalização do estupro mediante fraude
Bibliographic record
Abstract
The legal systems of countries such as Germany and the United Kingdom only allow punishment for the practice of a sexual act through fraud in certain situations. Conversely, art. 215 of the Brazilian Penal Code is broad enough to allow punishment whenever fraud constitutes a condition without which sexual consent would not have been obtained. Both models deserve criticism: the former is generally guided by the classic distinction between fraud in the factum and fraud in the inducement, which lacks clarity and foundation, and the later reaches conducts that should not receive any attention from criminal law. The present article discusses and criticizes these models with the aim of investigating the range in which the legislator is, in principle, authorized to criminally prohibit sexual fraud. Based on the understanding of the negative and positive dimensions of the right to sexual autonomy and the reach of the duties related to them, it is proposed to interpret art. 215 in a way that restricts the hypotheses of incrimination to those included in three groups: 1) frauds regarding the sexual nature of the act, the type of sexual act, and the identity of the person with whom the sexual act is practiced; 2) frauds that exercise coercive pressure or involve exploitation of a special trust relationship; and 3) frauds with the potential to cause harm (physical, financial, or emotional).
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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 | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.037 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".