Vulnerabilidade e perigo potencial – o processo de criminalização do assédio sexual online no Canadá e casos julgados em Ontário (2002-2014) / Vulnerability and its potential perils - on the criminalization of online luring in Canada and court cases tried in Ontario (2002-2014)
Bibliographic record
Abstract
A massificacao da Internet, nos idos de 1990, e o mais recente desenvolvimento e facil acesso as novas tecnologias de comunicacao e informacao trouxe a violencia sexual cometida no ambiente virtual como foco de atencao para aqueles preocupados com a protecao de criancas e adolescentes. Na esteira das discussoes sobre pornografia infantil, tomaram corpo as discussoes sobre grooming ou luring , traduzidos para o portugues como assedio sexual, aliciamento ou seducao online. Neste texto, reflito sobre o movimento de criminalizacao do chamado online luring ou online grooming no Canada, alem de relatorios judiciais de casos julgados por assedio sexual na Provincia de Ontario entre os anos de 2002 e 2014. A partir de 2002, a secao 172.1 do Codigo Criminal passou a proibir a comunicacao entre um adulto e uma crianca, via tecnologias da comunicacao e da informacao, quando esta pode ou poderia resultar em um crime sexual. Este artigo tem como foco nao o assedio em si, mas as crencas, valores e ideologias que estao em pauta, sendo discutidas as representacoes da crianca, do adulto agressor e do ambiente online, pilares que sustentaram o processo de criminalizacao do assedio online e que estao presentes tambem nos registros dos casos julgados por esse crime.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.023 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".