Declining nudes: Canadian teachers’ responses to including sexting in the sexual health and human development curriculum
Bibliographic record
Abstract
Addressing sexting in sexual health education classrooms is one way of supporting young people to become good sexual citizens and to emphasise respect and consent in their sexual practices and in their lives. While a fair amount of research has worked with youth to understand their motivations for sexting, less research has been conducted with in-service teachers to understand their perspectives, pedagogical approaches, and beliefs regarding young people and sexting. Set in this context, this paper discusses findings from interviews with Canadian teachers who were teaching a new Ontario Health and Physical Education curriculum that included discussions of sexting. Our findings suggest that many teachers are still engaging discourses of risk, shame and blame when they talk to their students about sexting. Likewise, longstanding gender norms and stereotypical sexual scripts are evident in the ways in which many teachers both understand and teach sexting. Some teachers, however, are engaging in more promising pedagogical practices that frame sexting as having a range of uses, outcomes, and purposes, painting a more holistic picture of young people’s sexting landscapes. Findings from this paper may be useful for educators and policymakers creating sexting curriculum for young people in educational settings.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".