Children’s Gender Worldviews: Exploring Gender, Diversity, and Participation Through Postmodern Picture Books
Bibliographic record
Abstract
Postmodern picture books offer valuable opportunities for children to engage with multiple perspectives and develop critical thinking skills. When used in pedagogical practices that prioritize children’s rights, agency, and voices, these books can effectively challenge dominant social norms and promote justice and equity. Within the framework of the SMOOTH project (Educational Common Spaces, Passing through Enclosures and Reversing Inequalities, Horizon 2020, EU), this qualitative study explores how children aged six to eight attending a public primary school in Lisbon, Portugal, make sense of gender through postmodern picture books. Grounded in the Educational Studies and Sociology of Childhood, the research analyses children’s understandings of gender and the meanings they construct concerning it. A six-month intervention program, consisting of read-aloud sessions, was conducted with children from diverse linguistic and socioeconomic backgrounds. Data were collected through focus groups and observation. Qualitative content analysis highlights how picture books can stimulate critical discussions on the social construction of gender, providing children with opportunities to reflect on differences, power relations, and social change. These findings indicate that embedding a care perspective further strengthens the recognition of children’s lived experiences and enriches educational practices by fostering inclusiveness and deeper understanding.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".