The Experiences of Youth from Immigrant and Refugee Backgrounds in a Social Justice Leadership Program: A Participatory Action Research Photovoice Project
Bibliographic record
Abstract
Research about the negative experiences of youth from immigrant and refugee backgrounds commonly emphasizes a lack of English language proficiency, criminal activity, and underachievement. More recently, a strengths-based, resilient, and social justice lens has been used to look at this historically oppressed population. In this research, I examined the experiences of immigrant and refugee youth in their involvement in a social justice leadership club in a secondary school in Calgary, Canada. I drew from Iris Marion Young’s theoretical framework using her five faces of oppression: exploitation, marginalization, powerlessness, cultural imperialism, and violence; and her four normative ideals of a deliberative model of democracy: inclusion, political equality, reasonableness, and publicity. I used photovoice and semi-structured interviews as part of the research design to work collaboratively with six female high school youth between 16 and 17 years of age to share their social justice initiatives with educational powerholders. The themes of identity and belonging, advocating for social justice, mental health awareness, and aspirational stance to dream emerged from photovoice participant analysis and interview data. I share the overarching themes of resiliency, self-efficacy, and empowerment; troubling Islamophobia; and reshaping the narratives of the school and community despite pressures to conform to the dominant culture. I also present future directions and recommendations to support youth from immigrant and refugee backgrounds in their social justice endeavours.
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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 | 0.011 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".