Considerações acerca dos discursos sobre a pedofilia e abuso sexual infantil: conceitos e diferenciações
Bibliographic record
Abstract
The article discusses the confusion prevalent on social media between pedophilia as a mental disorder and the crime of child sexual abuse. The lack of distinction between individuals with pedophilic disorder and aggressors without such a disorder is highlighted. The study uses Twitter as a source of analysis, collecting popular tweets between December 2019 and July 2020. The methodology combines literature review, exploring medical and legal concepts, and analysis of speeches on Twitter. The results highlight the mistaken association between pedophilia and crime, with opinions emphasizing the condemnation of the "pedophile" without considering clinical nuances. The term "pedophilia" is approached as a medical and legal diagnosis, differentiating it from crimes of child sexual abuse. The study concludes that the lack of in-depth debate on the topic contributes to automatic revulsion, hampering effective preventive proposals and highlights the need to consider treatments in addition to social remoteness to reduce risks of recurrence. The work highlights the importance of deepening the discussion on the conceptualization of pedophilia, considering its clinical and legal implications.
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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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".