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Record W7154916341 · doi:10.64166/j978xv44

The myth of the multicultural city

2010· article· W7154916341 on OpenAlexaffabout
Peter Fruchter, Amy Lavender Harris

Bibliographic record

VenueHagar Studies in Society and Culture · 2010
Typearticle
Language
FieldArts and Humanities
TopicPostcolonial and Cultural Literary Studies
Canadian institutionsYork University
Fundersnot available
KeywordsMulticulturalismNegotiationArgument (complex analysis)MythologyCultural identityIdentity (music)Cultural conflictNarrative

Abstract

fetched live from OpenAlex

Contemporary accounts of cultural conflict fixate on the material causes of global disparities, but overlook how culture originates and functions in lived places. Given that cultural conflicts play out increasingly not on international battlefields, but in the contested terrain of cities, there is an urgent need for analyses of conflict that take both culture and place into account. This paper explores how the problematics of identity and difference play out in Toronto, Canada, a diverse city where immigration has made unlikely neighbors of diasporas, reflecting the dynamics of three centuries of cultural conflict. Literary representations of culture, identity, racism and difference illuminate the importance of communicating across culture and underscore the paper's central argument that narrative itself can become a way of negotiating cultural conflict if it helps people put cultural difference on the table without coming to blows. Finally, this paper argues that tolerance, and such multiculturalism as tolerance makes possible, is vital to mitigating not only crises of identity in local places, but also cultural raptures and clashes in the global context.

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)0.003
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.151
Threshold uncertainty score0.300

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0240.102
Scholarly communication0.0170.008
Open science0.0010.011
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.031
GPT teacher head0.276
Teacher spread0.245 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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
Published2010
Admission routes2
Has abstractyes

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