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Record W4251453554 · doi:10.32920/ryerson.14647722.v1

Exploring Islamophobia in land use regulations: the case of the city of Mississauga

2021· preprint· en· W4251453554 on OpenAlexaffabout
Aaliya Khan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsIslamophobiaMemorializationImmigrationXenophobiaContext (archaeology)RacismSettlement (finance)Public administrationPolitical sciencePraxisSociologyPublic relationsGeographyLawBusinessArchaeologyPolitics

Abstract

fetched live from OpenAlex

Building community spaces is important for immigrant communities: it helps transform the sterility of an unfamiliar new city into home. Muslims have historically sought to make cities their own by building mosques that have served spiritual, social and symbolic functions as architectural sites. This essay examines what the interplay between settlement of newcomers and new types of land use applications and what it tells readers about municipal gaps in addressing the question of “difference” in Canada using the City of Mississauga as a case study. It starts with a review of the history and context of mosque building in Canada, follows up by examining land use policies in Mississauga, and then looks at controversial mosque development issues by examining official city and provincial documents including one Human Rights Complaint. This paper wraps up by presenting recommendations for municipalities to better navigate the question of difference through policy and praxis. Keywords: Islamophobia, land‐use planning, urban planning, racism, exclusion, xenophobia, Mississauga, public policy

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.504
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.483
GPT teacher head0.363
Teacher spread0.120 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2021
Admission routes2
Has abstractyes

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