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Record W2626182967 · doi:10.1080/14733285.2017.1334113

Youth geographies of urban estrangement in the Canadian city: risk management, race relations and the ‘sacrificial stranger’

2017· article· en· W2626182967 on OpenAlexafffundabout
Jo‐Anne Dillabough, Ee‐Seul Yoon

Bibliographic record

VenueChildren s Geographies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaMinistry of Education, Libya
KeywordsDisadvantagedSociologyGender studiesCritical race theoryEthnographyNarrativePoliticsRace (biology)ConversationMedia studiesPolitical scienceAnthropologyLaw

Abstract

fetched live from OpenAlex

This paper explores the impact of urban social divisions and changing race relations on the experiences of disadvantaged youth living on the periphery of two Canadian cities: Vancouver and Toronto. We analyze a cross-national subsample of 60 disadvantaged youths’ perceptions of urban social conflict and changing race relations in their city and school. We raise the larger question of how and why economically disadvantaged young people might embody particular understandings of safety, race, the other and security in different spatial registers of the city. We utilize an ethnographic methodology drawing from diverse but interrelated fields: border studies, the phenomenology of estrangement and a materialist version of critical race studies [(Ahmed, S. 1999. “Home and Away Narratives of Migration and Strangeness.” International Journal of Cultural Studies 2 (3): 329–347, Ahmed, S. 2010. The Promise of Happiness. Durham, NC: Duke University Press, Ahmed, S. 2013. The Cultural Politics of Emotion. London: Routledge; Kearney, R., and V. E. Taylor. 2005. “A Conversation with Richard Kearney.” Journal for Cultural and Religious Theory 6 (2): 17–26)].

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0130.006
Scholarly communication0.0040.001
Open science0.0010.005
Research integrity0.0000.001
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 head0.013
GPT teacher head0.253
Teacher spread0.240 · 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

Citations10
Published2017
Admission routes3
Has abstractyes

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