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Record W212301449

Claiming Space, Racialization in Canadian Cities

2006· article· en· W212301449 on OpenAlexvenueaboutno aff
Parastou Saberi

Bibliographic record

VenueCanadian ethnic studies · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsRacializationSociologyIdeologyGender studiesMulticulturalismRacismPoliticsHegemonyResistance (ecology)Race (biology)Political scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

Claiming Space, Racialization in Canadian Cities. Cheryl Teelucksingh, ed. Waterloo, ON: Wilfrid Laurier University Press, 2006. 210 pp. $32.95 sc. This interdisciplinary collection questions the assumption of racial harmony in Canada and within the ideology of Canadian multiculturalism. Emphasizing the proliferation of racial meanings in Canadian cities, the book explores the complexities of race and across diverse range of city spaces in Canadian metropolitan centers. Key to this collective contribution is an emphasis on the social analysis of as conceptual and interdisciplinary methodological tool for examining the history and politics of racialization. In this respect, chapters in this volume aim to provide evidence of in Canadian cities. As its main themes, it considers the various manners in which racial meanings become embedded in space, as well as people's claims to space. The authors also consider how the spatial configuration of Canadian cities are part of, and influenced by, racial domination and resistance. In her introductory chapter, Teelucksingh develops the notion of racialized space (p. 3) to consider the hegemonic social relations between people and dominant groups and institutions. She proposes claiming space, or new spaces, as a process whereby people attempt to create new identities and alternative representations, [manifesting] their resistance to the limits of the ideology of Canadian multiculturalism and the ongoing power relations associated with racialization (p. 3). Contributors theorize differently with respect to race, racism, and spatiality. However, they agree on the notion of racialized space as starting point, and then move on to consider how specific groups attempt to claim space. Ethnography, discourse analysis, and archival analysis are used--in varying degrees-by authors as methodological approaches to spatial analysis. Glenn Deer (chap. 2) examines the media-constructed moral (p. 19) about the growth of the Chinese Canadian population in Richmond, British Colombia, in 1995. He argues that public concern was linked to locally established Anglo-European Canadian entitlement to city space. He traces the roots of the panic in narratives about the early official (p. 19) history of Richmond in the 1970s. Kelly Train (chap. 3) highlights how Sephardic Jews in Toronto use Sephardic Kehila Centre as an alternative religious to both maintain their distinct identity and escape from racism and anti-Semitism within the broader society. For her, claiming is about both reinventing spaces and reconciling the tension between physical and symbolic space. In this respect, these spaces act as sale houses (p. 53) for their members. The relationship between diasporic identities and claims to is another issue examined by several authors. Anastasia Panagakos (chap. 4) explores the construction of nostalgic Greek identity in Calgary's Greektown. She indicates the importance of the Hellenic community center and its neighbouring landscape in providing sociable context within which ethnicity is recreated and identity remembered. …

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.135
Threshold uncertainty score0.981

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.011
Science and technology studies0.0240.018
Scholarly communication0.0110.003
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.076
GPT teacher head0.336
Teacher spread0.260 · 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

Citations49
Published2006
Admission routes2
Has abstractyes

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