Transforming Knowledge Hierarchies and Educating for Freedom: \n Incorporating Diverse Texts in Elementary Literacy Curriculum
Bibliographic record
Abstract
In this thesis I examine the potential of the intersections of critical pedagogy, new literacy studies and examples of qualitative ethnographic research of non-print based texts to transform elementary school literacy programs, educate for freedom, and allow more diverse voices and ways of knowing to be incorporated into the curriculum. I ask (a) Can an expanded understanding of “text” transform the literacy curriculum to allow students to develop and share their voices, be heard and to also learn from multiple perspectives? And (b) Can an expanded understanding of “text” contribute to the incorporation of a diversity of activities and experiences into elementary school literacy programs? With my theoretical foundation in Critical Pedagogy, I argue that western ways of knowing and the autonomous model of literacy are placed in a hierarchy above different ways of knowing and the recognition and application of the ideological model of literacy and multiliteracies. Furthermore, I argue for expanding our understanding of text to help transform hierarchical constructions of knowledge. I review literature in qualitative ethnographic research that demonstrates the use of non-print based texts for diverse multicultural education and I develop a framework to organize these diverse texts. Bringing together key elements from each of the theories I then begin to work towards a curriculum framework, including identifying foundational principles and offering strategies for how to practice teaching with diverse texts. The benefits for students, especially elementary aged children, and teachers are highlighted, as well as the challenges and future directions. \nKeywords: critical pedagogy, multiliteracies, texts, voice, elementary literacy, curriculum design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".