Content Analysis of LGBTQ Picture Books for Elementary Education Through an Intersectional Lens
Bibliographic record
Abstract
Diversity and inclusion have become a critical topic in contemporary society. Children engaging in story time with their family members and teachers can use stories from picture books as an educational tool to illustrate various social settings which provide a window into the wider outside world and a reflection of their own world, a concept known as “mirror and window”. However, LGBTQ representation has not been consistently perceived equitably compared to the heterosexual population, often dehumanized or unrealistic. Many LGBTQ students mentioned the need to see more portrayals of LGBTQ families and increasingly positive and realistic portrayals of LGBTQ characters. One independent publisher, Olly Pike, produces LGBTQ-inclusive picture books to support educators in providing representation and promoting an inclusive space in schools for diversity discussions. This case study utilizes content analysis of five LGBTQ-inclusive picture books from Olly Pike to understand the emerging themes that arise related to the support for LGBTQ and heterosexual students and foster an inclusive space in schools, leading to themes of diversity and representation that benefits critical discussions in schools and classrooms. Inter-rater reliability was established between the two authors for validity of emerging themes. Results revealed seven emerging themes: 1) Diversity and Inclusion (e.g. family structures, sexuality, ethnicity), 2) Daily Life, 3) Anthropomorphism, 4) Stereotypes, and 5) Overarching Educational Goals. Implications of findings reveal the benefit of incorporating LGBTQ-inclusive picture books to facilitate all students’ learning and understanding of SEL skills that largely tie with becoming responsible citizens that is respectful of diversity and specifically for LGBTQ students to see themselves positively represented, fostering a sense of belonging.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".