Teachers’ Use of Children’s Literature that Accurately Portrays Individuals with Exceptionalities in Inclusive Classrooms
Bibliographic record
Abstract
This study investigated how teachers’ use of children’s literature that accurately portrays individuals with exceptionalities might play a role in supporting an inclusive learning environment. Students with exceptionalities are a reality for the majority of teachers in Ontario, and children’s literature can be a powerful tool for learning. Children’s literature can allow educators to help connect with their students and can help students understand the world around them. Children’s literature that accurately portrays individuals with exceptionalities can help teachers teach understanding and acceptance to their students. Through this research, face-to-face interviews with three teachers in Ontario were conducted, and inclusivity strategies currently in place for students with exceptionalities in general education classrooms were investigated. Preliminary findings showed that teachers use a variety of strategies to make their classrooms more inclusive for all students, teachers promote inclusion in their classrooms through: a) sharing and generating discussion about specific children’s literature containing accurate portrayals of individuals with exceptionalities and c) encouraging understanding and empathy; and b) open discussions with all students. Thirdly, Teachers face a lack of funding when it comes to support and resources for students with exceptionalities. The last theme that emerged from the data was that teachers face a lack of awareness and knowledge about support and available resources such as children’s literature portraying characters with disabilities.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".