Exploring the Experiences of Racialized Immigrant Mothers in Contributing to Patterns of Community Life in the Victoria Hills Neighbourhood
Bibliographic record
Abstract
The experiences of racialized immigrant mothers are complex, nuanced, and beautiful. Oftentimes, their stories are described in a way that frames women as helpless victims, merely writing about their challenges in the absence of their protagonism. The stories in this thesis suggest that, against the obstacles unique to racialization, motherhood and immigration, mothers are often the primary caretakers of their families and play a significant role in the development of children, while creatively exploring and responding to questions pertinent to community development. They navigate community life in a place different from the one they grew up in – addressing questions around how to belong and battle isolation, how to shape and contribute to patterns of life that are at once parallel to experiences from back home yet interlinked with existing elements of culture, and how to create conditions that nurture the spiritual and intellectual development of children. These are areas of learning that have been the focus for many residents in Victoria Hills, a neighbourhood in Southern Ontario that has received large influxes of immigrant families for several decades. As such, this thesis highlights and explores the lived experiences of racialized immigrant mothers within the scope of Victoria Hills, displaying how central they are in contributing to vibrant communities and raising generations who will shape future society. This project employed a qualitative research paradigm, incorporating elements of ethnographic and community-based research, and drawing on various theoretical frameworks, including social constructivism, transformative worldview, critical race theory and intersectional feminism. At a time of increasing global isolation and rampant individualism, drawing insight from the experiences of these mothers is pertinent to anyone who wishes to contribute to the development of communities that serve as safe havens. Additionally, exploring factors that enable and hinder a mother’s pursuit of her aspirations for herself, her children, and her community might incite a critical discussion about how to better support mothers as protagonists.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".