Surfacing the Assumptions: Pursuing Critical Literacy and Social Justice in Preservice Teacher Education
Bibliographic record
Abstract
This paper outlines a four-year study of a preservice education course based on a socioconstructivist research framework. The preservice English Language Arts course focuses on critical literacy and teaching for social justice while employing digital technologies.The research study examines two concepts across all aspects of the course: 1) new literacies and multiliteracies; and 2) technology-supported transformative pedagogy for social and educational change. While the authors originally undertook the study to evaluate separate assignments of the course, the lens of the two themes has provided an opportunity for a scholarly review of their teaching practices. Research data include three course assignments over a 2-year period; an open-ended survey; and focus group and individual interviews with pre-service teachers. The authors discuss some of the affordances, challenges, and learnings associated with preparing teachers to teach critical literacy in a digital age. They also consider the development of critical literacy skills which encourage preservice teachers to bring their literacy histories and assumptions to the surface, examine them critically, and consider social justice alternatives.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".