Reading straight : an examination of heteronormativity in children's literature
Bibliographic record
Abstract
Diversity in ignored in many early childhood education settings, especially sexual diversity. The purpose of this study is to determine if children's literature in early childhood education settings represents diversity. Because identity is interconnected, this study includes an exploration of individual aspects of identity such as gender, sexuality, race, class, age, body size and ability. One hundred and nine books from four childcare classrooms were analyzed to investigate the representation of diversity in children's literature. A qualitative queer critique of two of the titles from this study supplemented the competing yet inadequate findings of the quantitative research. Results showed that beyond moderate representation racial diversity, the literature examined failed to represent significant diversity of sexuality, class, age, body size, and ability. Through the analysis of children's books it was found that oppression exists in the form of omission. Research from supplementary queer critiques of two titles showed that each book is heteronormative in nature and that one of the books may be deemed homotolerant as it positioned heterosexuality as 'normal,' represented sexuality as private, failed to celebrate difference, and failed to challenge essentialism. The findings of this study may be significant for initiating a dialogue among early childhood professionals to promote a celebration of difference.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".