Literacy Teacher Educators Creating Space for Children’s Literature
Bibliographic record
Abstract
This paper reports on a qualitative research study that examined how 10 literacy teacher educators (LTEs) utilized children’s literature to invite teacher trainees to critically engage with social issues, challenge their assumptions about literacy, and begin to develop the knowledge and dispositions to work alongside diverse learners (e.g., culturally, linguistically, socio-economically). The LTEs recognized that teacher trainees often entered their literacy courses with restricted conceptions of literacy and deficit assumptions about children from economically marginalized and/or culturally and linguistically diverse backgrounds. Within their courses, the LTEs positioned literacy as a multifaceted social practice, wherein access to a variety of representational resources facilitates the active construction of knowledge and identities. The LTEs modeled instructional strategies and designed assignments that encouraged teacher trainees to use children’s literature as a means to connect with issues relevant to the lives of young learners within contemporary classrooms. This research will be of interest to LTEs who endeavor to use children’s literature as a springboard to support teacher trainees to develop a self-reflective stance and a critical cultural consciousness.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".