Bibliographic record
Abstract
This article discusses how the comics form is peculiarly suited to deliver affecting, inclusive sex education. Through analysing the comics anthologiesNot Your Mother’s Meatloaf, compiled by Saiya Miller and Liza Bley, andGraphic Reproduction, edited by Jenell Johnson as part of the Graphic Medicine series, this article addresses several specific ways in which these anthologies – and the autobiographical comics they include – demonstrate unconventional and effecting methods of conducting sex education. Comparing these collections to sex education film shows how comics are particularly suited to this goal. These comics anthologies demonstrate the importance of inclusive community-building as a central project of sex education, as well as the need to challenge the teacher–student methodology. Specific comics within these anthologies by Eli Brown and Paula Knight demonstrate how comics allow for radical expressions of how bodies relate to sex and sexuality. Other comics, including those by Alison Bechdel and Alex Barrett, reveal how the pauses and ambiguities fostered by comics heighten their emotional impact and educational value. The overarching power of these narrative comics comes from the self-awareness of the form itself, especially the vulnerability of drawing oneself in relation to sexual experiences. This article concludes that these distinct characteristics of comics allow both a healthy way for creators to look back on their own experiences with sex and, in turn, encourage readers to effectively learn from these depicted experiences.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".