Sex is a Funny Word: A Book about Bodies, Feelings, and YOU by C. Silverberg & F. Smyth
Bibliographic record
Abstract
Silverberg, Cory and Fiona Smyth. Sex is a Funny Word: A Book about Bodies, Feelings, and YOU. New York: Seven Stories Press, 2015. Print.“Having a penis isn’t what makes you a boy. Having a vulva isn’t what makes you a girl. The truth is much more interesting than that!”In a watershed year for transgender activism, Cory Silverberg and Fiona Smyth have broken ground with their trans-inclusive sex education book Sex is a Funny Word. This new release follows a cast of four 8-10 year old kids, including gender queer Zai, as they learn about and explore that strange and funny word - sex. Throughout the book, Smyth’s illustrations employ bright, opaque and high-contrast colours to great effect, and readers are exposed to characters of wonderfully diverse abilities, races, shapes and sexualities.It is however reductive to merely draw attention to the diversity in Sex is a Funny Word, as a wide gamut of topics are discussed with age-appropriate aplomb, including anatomy, “touch” and relationships. Silverberg’s opening notes to adult readers provide tips for co-reading the book with children, explaining that the text is meant to be read over many sittings to aid comprehension and reflection. By bookending each chapter with interactive questions, readers identify and reflect on personal morals anchored in four concepts - trust, respect, joy and justice. These questions do the hard work for parents, who can defer to one of the lively and inquisitive characters for discussion points. Moreover, the only sexual activity mentioned in this book is masturbation, so even the most squeamish adult readers should feel comfortable co-reading Sex is a Funny Word.Sex is a Funny Word is the second of a three-part, age-specific sex education series written by Silverberg and illustrated by Smyth. Those with younger children can find reproduction discussed in What Makes a Baby, while intercourse will specifically be covered in the series’ next title. For a positive, fun and healthy introduction to these concepts, parents and librarians will be well-served by seeking out Sex is a Funny Word for their collection.Highly Recommended: 4 out of 4 starsReviewer: Kyle MarshallKyle Marshall is the School-Aged Services Intern Librarian for Edmonton Public Library. He graduated with his MLIS from the University of Alberta in June 2015, and is passionate about diversity in children's and youth literature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.047 | 0.025 |
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".