Sex(ting) Education: Analysing Quebec education concerning young people’s digital sexual media production
Bibliographic record
Abstract
Young people have faced a history of moral panics concerning their presence online, and a recently reported increase in teenage sexting has intensified these fears. As a result, responses from stakeholders have told young people that their exploration of sexuality, particularly mediated \nthrough digital technology, is wrong, often correlating their behaviours with deviance and illegality. Yet, providing agency to young people in their sexual media production enables teenagers to communicate their sexual desires. As use of digital technology continues to accelerate among the youth sector, we must understand that mediating one’s sexual life will be included in its use. \n \nThis thesis project explores how Quebec police departments attempt to educate young people (eighteen years and younger) regarding the practice of sexting, primarily the sharing of nude photographs. This project investigates Quebec’s sexting campaigns “SEXTing is PORN,” and “SEXTO,” developed to shape adolescent access to digital sexual information. Campaign material is examined through situational analysis to identify discursive ideas of sexting presented in these pieces of media, the collection of stakeholders involved in the creation of these messages, \nand the potential absence of positions within these discourses of teenage sexting. Overall, this research finds that the campaigns under study rely on police and government organizations framing teenage sexting as child pornography. Without expressing alternative positions, young people are left unequipped to deal with challenges they may face and may be scared to reach out in fear of legal repercussions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".