"When you witness an evil act, you should stop it with your hand." Citizenship Learning and Engagement of Muslim Youth Activists in Toronto, Canada
Bibliographic record
Abstract
This thesis is about being young, Muslim and politically engaged in contemporary Toronto, Canada. Young Muslims attempting to come to terms with the complex and contradictory promises of Canadian citizenship must confront what it means to "be Canadian"—a national identity marked by historical legacies of oppressions, and shaped by ideals of western liberal democracy. Post-9/11 Canada is marked by intensified suspicion and repression of Muslims and those who “look like” Muslims. This thesis examines how 18 young people, ages16 to 29, who self-identity as “Muslim” and “activist” learned “to reflect and act upon the world in order to transform it” (Freire, 1970). Through life history interview methods, this study attempts to capture how the participants had come to their political activism, critical experiences of learning inside and outside of schools that they understood as influential in shaping their political subjectivities and practices, the range of issues of injustice that concerned them, and the various actions they took to address those issues. The young Muslims expressed concern for and acted on access to quality affordable housing, police brutality, gender-based violence, Islamophobia and other forms of hate, and the question of Palestine. Their actions included creating safe spaces, (dis-) engaging formal systems of governance and public authority, providing public education, producing cultural narratives, and engaging in various forms of direct action. Their voices and stories maintain centrality throughout this work. This thesis is based on a broad definition of “education” that encompasses formal and non-formal education and informal learning. It is also based on the premise that “all education is citizenship education.” It demonstrates how the young Muslims’ multiple learning experiences in families, neighborhoods, communities, youth subcultures, social movements and school--embedded in histories of war and migration—enable them to name and to take action to transform the concrete situations of oppression that impact them and their communities. Particularly important for the young Muslims were the cultural and political spaces in which they were able to critically and collectively explore and question their lived experiences, identities, and binding solidarities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.009 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".