Dispositions for Inclusive Literacy: Fostering an Equitable and Empowering Education for Academically Diverse Learners
Bibliographic record
Abstract
This article offers teacher educators’ practical methods for, and shares findings from a study of, developing teachercandidate dispositions for inclusive literacy. Based on the extensive teacher disposition literature, the authors discernthat dispositions for inclusive literacy include the belief that all students have valid ways of being literate; the valueof inclusive literacy experiences for all students; and an attitude that all students should be participants in meaningfulliteracy experiences. Using a within-site case study approach, qualitative thematic analysis of three assignments usedin a literacy teaching methods course suggest that it is possible to shift narrow dispositions to broader and moreinclusive conceptualizations that support struggling readers and students with disabilities in the general educationclassroom. Conclusions suggest that dispositional development toward inclusive literacy can support teachercandidates’ implementation of inclusive literacy practices; thus, fostering an equitable and empowering education foracademically diverse learners.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 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".