Immigrants' Sense of Belonging in Diverse Neighbourhoods and Everyday Spaces
Bibliographic record
Abstract
In this thesis, I examine issues of immigrant belonging, such as feelings of being accepted, recognized, and trusted, as well as having a sense of community and support. The focus is on the context of the neighbourhood in shaping immigrant sense of belonging. Neighbourhoods in Canadian cities are the locus of multiple structural forces – among these the provision of housing, services, infrastructure, and community that contribute to meeting everyday needs and feeling included, but also of discrimination, marginalization, and exclusion through processes of enclosure, neoliberal urban policies, gentrification, and revanchism. In my thesis, I use spaces of encounter as the theoretical framework to examine immigrants’ sense of belonging. Through narratives of belonging and not belonging, I aim to capture the full complexity of immigrants’ sense of belonging. To do so, I adopted a collaborative qualitative approach combining multiple methods, including critical ethnography, descriptive Census data analysis, media analysis, and photovoice interviews with 13 immigrant men and women from diverse countries of origin. The neighbourhood selected for this study is Ledbury-Heron Gate, a low-income, immigrant neighbourhood in Ottawa. Subject to stigmatization and mass evictions, Ledbury-Heron Gate is a contentious space and the site of struggles between residents, mainstream media, developers, and city officials. Yet, many participants have found amenities, mutual support, and solidarity in the neighbourhood that they have come to appreciate and value. I present a narrative of Ledbury-Heron Gate that is not portrayed elsewhere, a complex and sometimes contradictory story of belonging and not belonging. My findings reveal that sense of belonging is not simple, and there can be simultaneous feelings of comfort and recognition combined with resentment and fear. I emphasize the participants’ accounts of agency and knowledge among the residents of iii Ledbury-Heron Gate and their ability to create spaces where they can build community and solidarity. Yet, they encounter challenges such as (in)accessibility, discrimination, and disinvestment. Based on the narratives that I will recount in this thesis, it will become clear that the participants are keenly aware of the barriers that they face; yet they refuse to let these determine their sense of belonging and support for one another. Their efforts, with proper structural support from various levels of government, local institutions, and NGOs, hold the potential to transform spaces of encounter into spaces of empowerment and connection. Their complex and at times contradicting narratives of sense of belonging and experiences of exclusion show the nuances that come from being an immigrant trying to belong to a place that is not always inclusive.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| 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".