Who am I?: The Emotional Situations and Identity Constructions of Canadian-Born Ismaili Muslim Youth
Bibliographic record
Abstract
This dissertation examines the inner work of identity formation as it takes shape for minoritized, and often marginalized, Shia Ismaili Muslim adolescents. Through the use of psychoanalytic theory and qualitative research methods, including focus groups and individual interviews, the emotional world of adolescents is analyzed to foreground conflict, difficult feelings and intergenerational memories. Identity markers of faith, culture, race, and citizenship are explored through the psychoanalytic concepts of anxiety, loss, melancholia, guilt, and ideality. My analysis focuses on how social contexts of prejudice and stereotypes relate to inner experiences of isolation, loneliness, and feeling misunderstood. Focusing on the emotional dynamics of faith identity, the dissertation offers an account of the creative and at times defensive processes through which adolescents navigate relationships with teachers, parents, peers, media, and school in a Canadian context that meets, but also fails to meet, their efforts. While highly attuned to the ways Islamophobia operates in public discourse in Canada, the participants have difficulty acknowledging their distress, struggle to find hope and spaces of inclusion, and take on the weighty responsibility to educate others in an effort to reduce the hate projected onto them. The result is a painful split between their faith and their secular selves.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".