Young Readers and Sexualized Fiction: A Review and Case Study of Young Women in Canada
Bibliographic record
Abstract
Abstract In sexualized fiction written for young readers (ages 12–24), common narratives are meant to guide readers through their early sexual life, inform about the dangers and pleasures of sexuality, and validate the sexual lives of those deemed outside of the heterosexual norm. Young Adult (YA) novels comprise most of the canon of literature for readers of this age, but what young people read extends into other genres and formats, such as Adult fiction, graphic novels, and online amateur fiction. Literary scholars have criticized sexualized fiction for young readers for the presence of a sexually repressive ideology embedded within sexual relationships or scenarios. Most notably, didactic narratives meant to guide young persons through their early sexual life have typically associated sex with risk and not necessarily with pleasure or well-being, especially for young women. The argument, therefore, is that these texts are detrimental to the sexual well-being or liberation of young people. Contrary to this argument, sexualized fiction for young readers has also been subject to widespread censorship efforts in North America. Challenges or bans are typically based on concerns that these texts are pornographic, are unsuitable for young readers, or will inspire young people to act on their sexual impulses or engage in non-normative forms of sex. These two counterarguments parallel larger debates about what kinds of information about sexuality young people should have access to or how young people should perform—or not perform—their sexuality. The study of reading in the everyday lives of young people reveals complex relationships between text and reader, beyond those commonly cited as essentially repressive or corruptive. A case study on the reading experiences of young women in Canada shows that young readers engage with a wide variety of sexualized fiction and have nuanced relationships with these texts. This case study shows that as a literature that addresses the complexities of adolescent sexuality, sexualized fiction remains a source for transformative possibilities, where fictional narratives have the possibility to contribute to the sexual well-being of young people.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".