Negotiating Space, Emotion, and Belonging: A Critical Socio-Spatial Approach to Iranian Immigrant Integration in Toronto
Bibliographic record
Abstract
This doctoral thesis aims to reconceptualize immigrant settlement narratives through a critical socio-spatial lens, focusing on the case of established, predominantly economic-class Iranian immigrants in Toronto, Canada. By interrogating residential choices, integration pathways, community-building practices, and homemaking strategies, the study challenges traditional models of immigrant settlement, foregrounding the agency of skilled Iranian immigrants in shaping spaces of belonging within a multicultural metropolis. The analysis integrates Lefebvre's (1991) theory of the social production of space, Askins' (2016) concept of emotional citizenry, and social capital theory (Putnam, 1993; Woolcock, 1998) to analyze how Iranian immigrants negotiate place and identity. The thesis comprises four interconnected articles. The first interrogates residential concentration in Toronto's affluent northern suburbs through Lefebvre's spatial triad, challenging existing models of immigrant settlement and suburbanization. Using Askins' emotional citizenry, the second article examines the complex interplay between residential and socioeconomic integration, highlighting gendered experiences and emotional belonging. The third investigates multiscale community-building practices, showing how bonding, bridging, and linking social capital, in combination with emotional citizenry, foster affective attachments across private and public spheres. The fourth article frames homemaking as a spatial practice that, through Lefebvre's triad, actively reconfigures suburban homes and environments into contested sites of cultural preservation and identity negotiation. Employing a mixed-methods approach - including census data analysis, 54 semi-structured interviews with photovoice, ethnographic observations, and 7 key informant interviews - the methodology aligns with the conceptual framework's emphasis on spatial production and affective agency. The study makes three key contributions. First, it offers an integrated conceptual framework bridging structural spatial analysis (Lefebvre) and emotional agency (Askins), challenging deficit-oriented settlement models. Second, it repositions integration as an active, multidimensional process through which immigrants leverage (suburban) spaces to reconcile cultural heritage with social mobility aspirations. Finally, it provides a multiscalar account of how immigrants mobilize social capital across spatial and institutional contexts to foster emotional belonging.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".