“Now I feel ‘truly’ like me!”: a discourse analysis of the ways ‘the gaze’ functions in two children's picture books featuring transgender and gender variant characters
Bibliographic record
Abstract
Drawing on transgender, queer and feminist theoretical perspectives, I critically analyze two children’s picture books featuring transgender and gender variant characters. With these critical theoretical perspectives in mind, this discourse analysis examines the ways the books, both visually and textually, depict gender embodiment and the experiences of the characters. Using questions derived from these theoretical lenses, I analyze concepts of power, normalcy, difference, the gender binary, gender fluidity, intelligibility and unintelligibility. These concepts contribute to the dominant discourse of ‘the gaze’, seen in varying ways in the books. Children’s story books largely underrepresent the experiences of transgender characters, particularly books outlining, and explaining, a social gender transition. The majority of picture books with LGBTQ+ themes focus on same sex families and feature boys in dresses, thus centralize around disrupting the constraints of masculinity. I conclude this paper with recommendations for selecting, reading, and discussing books with transgender and gender variant protagonists. The central themes outlined in the academic literature illustrate that ‘the gaze’ and regulation of knowledge have a significant impact on what is visible in children’s books. This may ultimately affect children’s understanding, and appreciation, of gender variance and, hence, social gender transitions in early childhood.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".