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Record W2967639706 · doi:10.1108/ijssp-11-2018-0198

Mythical encounters: challenging racism in the diverse city

2019· article· en· W2967639706 on OpenAlexaffabout
Shana Almeida

Bibliographic record

VenueInternational Journal of Sociology and Social Policy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRacismSociologyCommodificationFraming (construction)Diversity (politics)PoliticsContext (archaeology)DemocracyNarrativeValue (mathematics)Gender studiesOriginalitySocial sciencePolitical scienceLawAnthropologyQualitative researchGeography

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to situate the idea that the City of Toronto is a leader on addressing issues of diversity, racism and democracy within the context of diversity discourse and the racial norms that are incited by it. Design/methodology/approach A genealogy and critical discourse analysis of City of Toronto documents from 1975 to 2017 involving consultations with racial Others on issues of diversity, race and/or racism was conducted. Findings The author shows how the specific racial norms that continue to make up diversity discourse as “truth” in the City of Toronto are reproduced through the commodification of racial Others and the re-framing of their racial claims, to re-generate the narrative of the diverse City of Toronto where all are welcome to participate and belong. Practical implications Implications for senses of place are discussed. Originality/value This paper adds critical depth to understanding increased participation of racialised communities as a mechanism for achieving socio-political change in government and non-government institutions. Initiated in a local context, the findings of this paper are intended to contribute to a global reservoir of critical knowledge on diversity, race, democracy, political participation and power.

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)0.000
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 score0.594
Threshold uncertainty score0.172

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.033
GPT teacher head0.365
Teacher spread0.333 · 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

Citations15
Published2019
Admission routes2
Has abstractyes

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