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Record W4402186225 · doi:10.32920/26866423

The Power of Care: A Case Study on the Socio-Spatial Navigation of Muslim and Arab Immigrant Women in the City of Mississauga

2024· preprint· en· W4402186225 on OpenAlexaffabout
Salma Abdalla

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsImmigrationPower (physics)GeographyGender studiesPolitical scienceSociologyArchaeology

Abstract

fetched live from OpenAlex

Background: Drawing from interdisciplinary areas of scholarship and philosophical Islamic principles (Mortada, 2003) informing responsible community building processes, this research presents a contextual assessment that explores various underlying complexities impacting the lived experiences of Muslim immigrants in Canada. Scholarship on theoretical/conceptual frameworks of care and compassion is limited in planning literature and development praxis (Berkley, 2020). Literature on lived experiences of Muslims in North American cities have two major gaps: (1) the generalization of complex Muslim identities (Ali, 2008), and (2) the impacts of peripheralized formal care networks (Hackworth et al, 2012). In response, I aim to understand the barriers and facilitators to (re)building belonging and identity in a place of promised peace, freedom, and permanence for Muslim Arab immigrant women in Mississauga, a growing population group. Research Questions: 1) What are the Muslim and Arab immigrant women's networks of care in their communities and how do such spaces impact their sense of identity and belonging in place? 2) What are the social and physical barriers and facilitators to accessing formal and informal networks of care that foster a sense of belonging? Methods: I recruited eight participants who visibly-identify as Muslim (wear a Hijab or head veiling) Arab immigrant women living in Mississauga and used Sketch and Social Network Mapping (Gieseking, 2013), to enable them to share their perceived facilitators and barriers while seeking to (re)establish a sense of belonging, identity, and formal/informal networks of support in a new home away from home. Results: The findings reveal that the majority of formal spaces of care noted by participants (e.g., Mosques, Islamic classes, and age-friendly Islamic activities) are peripheralized from residential landscapes through discriminatory policy and zoning codes contesting their existence. Such places, however, are deemed integral in cultivating social and spiritual well-being, as well as a shared sense of belonging and attachment to place over time. This peripheralization not only imposes spatial barriers to equitably accessing formal and informal networks of specialized care and support (e.g., lack of public transportation, lack of age-specific services, etc.), but also hinders participants’ ability to feel like they belong in place, thus depleting their emotional well-being. Conclusion: This research begins to address the need to explore the diverse social identities of Muslims in Canada in variegated geographies. This is done through getting beyond the generalization/misinterpretation of their everyday lives and providing contextual understandings that meaningfully represent the complex lived realities of people, in place, across time. For planning practice, this information leads to recommendations for land use and formal care services planning policy changes, as well as the need to educate planners on these complexities to conduct meaningful engagement and build truly inclusive cities.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.728
Threshold uncertainty score0.540

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0250.008
Scholarly communication0.0030.002
Open science0.0030.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.020
GPT teacher head0.317
Teacher spread0.298 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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