This Land is<i>Our</i>Land? Multiple Literacies and Becoming-Citizen in an Adult ESL Classroom
Bibliographic record
Abstract
Unprecedented levels of global migration have produced pluralistic nations where citizenship education can pose a complex challenge. In these contexts, government-funded language instruction programs for new immigrants have become important sites, where politically charged debates around citizenship and how to teach it play out. This article considers the intersections of citizenship education, power, multiple literacies, and curriculum in a Canadian adult immigrant language program mandated to facilitate the integration of newcomers. Deleuze & Guattari's conceptual repertoire, along with the Deleuzian-informed Multiple Literacies Theory, frame an analysis of qualitative data focusing on a singular classroom event: the singing of a folk song. This research follows lines of power: state power as pouvoir operating through the order-word ‘multiculturalism’, and life's affective power as puissance operating through reading texts disruptively in the classroom. These molar lines and lines of flight run between nation-state citizenship as an integrative outcome premised on sameness, and the concept of ‘becoming-citizen’ as an untimely process premised on difference. Considering the implications of this analysis for classroom practice, ‘rhizocurriculum’ is posited as a way to reimagine citizenship in ways that can account for the revolutionary, transformative effects of difference.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".