Experiences of Multiple Literacies and Peace: A Rhizoanalysis of Becoming in Immigrant Language Classrooms
Bibliographic record
Abstract
This dissertation uses Masny’s Multiple Literacies Theory (MLT) to problematize assumptions about literacy underpinning the Language Instruction for Newcomers to Canada (LINC) program. In addition to teaching English, LINC aims to orient adult immigrants to “the Canadian way of life” which I argue constitutes a form of peace education: teaching peaceful, multicultural values as part of being/becoming Canadian. How do language, literacies, and lessons about peace intersect? MLT foregrounds the Deleuzean-Guattarian concept of becoming: reading intensively and immanently disrupts and transforms individuals in unpredictable ways. I deploy Deleuze and Guattari’s war machine to think about peace as a text that is read and violence as a revolutionary, disruptive force essential for the invention of peace. Accordingly, this research focuses on how experiences of peace AND violence contribute to becoming (i.e. transformation) through reading, reading the world, and self in LINC. Over a 4 month period, 2 teachers and 4 students participated in qualitative inquiry strategies including: video-recorded classroom observations, individual interviews (based on the viewing of video footage of classroom events), and student audio journals. I also collected classroom artifacts used during the observations. Through Deleuze’s transcendental empiricism I frame my research approach as rhizoanalysis. Rhizoanalysis is a (non)method that views data as transgressive (exceeding representation), analysis as a process producing rhizomatic connections (immanence), and reporting as cartography (mapping different assemblages). This research affirms that there is more going on in LINC than its mandate implies and raises questions pointing to the complexities of teaching and learning English in LINC. How might lessons about multicultural values be taken up in ways fraught with tensions between peace AND violence? Becoming-Canadian is an event that unfolds through reading, reading the world, and self. As sense emerges, how might the collision of worldviews around experiences of peace AND violence create encounters that potentially disrupt? How are students, teachers, and even the concepts of “Canadian” and “peace” transformed? I posit rhizocurriculum as a way to account for the affective and transformative powers of multiple literacies in language learning and to view adult immigrant language classrooms as sites of experimentation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".