Deleuze and becoming-citizen: Exploring newcomer films in a Franco-Canadian secondary school
Bibliographic record
Abstract
Abstract Citizenship and citizenship education have been traditionally bounded to either a geographically bound nation-state or a historically shared culture. In this article we argue that it is no longer enough to explore the complexity of what we term becoming-citizen in today’s information-based society where multiple national and cultural connections and affiliations are a mouse click away. We make the case for the importance of understanding how developing literacies affect how citizenship is transformed in pedagogical settings, particularly in terms of how Information and Communication technologies (ICT), the curriculum and teaching intersect and affect each other as complex systems. To do this, we use Deleuze and Guattari’s concept of agencement and Multiple Literacies Theory (MLT) to map how citizenship emerges in a group of young newcomer students’ texts (broadly defined) as filmed with pocket size digital video cameras. The research reported here comprises part of a three-year research project on the interrelationships between citizenship, technology and pop culture in a French secondary inner-city Ottawa schools. We begin with an outline of the concepts of agencement and MLT. We then briefly summarize the current literature on citizenship education before proceeding to an account of how our research has been guided by rhizoanalysis. We then proceed to three vignettes associated with the curriculum used in the particular school under study and two video clips shot by two newcomer students. We conclude with a discussion of the implications of our study in terms of theory and practice.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".