The Crescent Moon through Maple Trees: How Racializing Settlement Structures Reinforce Nonbelonging, and Perceptions of ‘Canadianness’ for Pakistani Newcomers
Bibliographic record
Abstract
In this dissertation, I develop nonbelonging as a sociologically relevant concept and heuristic by examining what it means to be “Canadian” for newcomers. Drawing on life-history interviews with newcomer Pakistani immigrants and first-generation settlement counselors, I show how nonbelonging is subtly reinforced during early settlement. First, I use Foucault’s concept of governmentality to analyze how the settlement service sector legitimizes nonbelonging at an institutional level. I demonstrate how institutional practices and social interactions reinforce nonbelonging, even in seemingly inclusive social institutions. Next, I apply Bourdieu’s theory of symbolic capital to examine how newcomers experience nonbelonging through the loss or devaluation of professional and educational credentials, linguistic fluency, and social status. These accounts reveal how structural and embodied forms of nonbelonging intersect. Finally, I use a postcolonial framework to show how whiteness continues to define authentic Canadianness. I analyze how Canada’s image as an “authentically” white nation reinforces racialized exclusion and structures perceptions of belonging (Mackey 1999; Thobani 2007), and how these perceptions coexist with nonbelonging. By centering the voices of Pakistani newcomers, this dissertation provides the first comprehensive study of how nonbelonging operates and is reinforced in latent ways for racialized immigrants in Canada. I argue that nonbelonging is not an unintended consequence of immigration but an active, structured, and ongoing condition that functions alongside racialized belonging. Across three studies, I demonstrate that institutions which appear inclusive, such as settlement service centers, often communicate messages of nonbelonging. Settlement counselors, who are themselves first-generation immigrants, draw on their own experiences of exclusion, normalizing these barriers for newcomers. For Pakistani immigrants, this process becomes embodied in the loss of symbolic capital and felt through contradictions between pre-migration expectations and post-migration realities. These interactions reflect and reproduce broader structures of whiteness that continue to define Canada’s national ethos. Ultimately, this dissertation shows how Canada’s immigration system simultaneously welcomes and excludes, producing a complex and contradictory experience for racialized newcomers like those from Pakistan. It makes visible the hidden processes of nonbelonging, challenging assumptions of inclusivity and calling for deeper sociological engagement with the intersections of race, migration, and belonging.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".