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Record W2322006537 · doi:10.1177/1466138113513526

‘You’re making our city look bad’: Olympic security, neoliberal urbanization, and homeless youth

2013· article· en· W2322006537 on OpenAlexafffundabout
Jacqueline Kennelly

Bibliographic record

VenueEthnography · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEthnographySociologyUrbanizationNeoliberalism (international relations)ReputationGender studiesCriminologyEconomic growthPolitical economySocial scienceAnthropology

Abstract

fetched live from OpenAlex

Drawing on ethnographic research with homeless and street-involved youth in Vancouver before, during, and after the 2010 Olympic Games, this article offers a portrait of neoliberal urbanization as experienced by a city’s most marginalized residents. Taking as paradigmatic the aspirational goals of Olympic host cities to enhance their reputation as ‘global cities’, the article explores what this means for homeless youth through three processes: city cleansing, city marketing, and self-regulation. Examining how each of these are imbricated with policing and security practices, the article offers an in-depth look at how these abstractions are lived by homeless youth in the everyday. The article concludes by suggesting that marginalized young people are not the beneficiaries of Olympic legacies, despite promises made by organizing committees. In contrast, findings indicate that homeless young people are further marginalized by the Olympics, providing support for previous research that aligns mega-events with neoliberal outcomes.

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 score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.309
Teacher spread0.273 · 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

Citations40
Published2013
Admission routes3
Has abstractyes

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