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Record W1655151866 · doi:10.3138/topia.17.51

Youth Make Nations: Three Toronto Stories

2007· article· en· W1655151866 on OpenAlexvenueaboutno aff
Andil Gosine

Bibliographic record

VenueTOPIA Canadian Journal of Cultural Studies · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsnot available
Fundersnot available
KeywordsYouth cultureGender studiesDiasporaNarrativeNationalismSociologyArticulation (sociology)ClubIdentity (music)Representation (politics)NegotiationYouth studiesMedia studiesPolitical scienceAestheticsArtSocial sciencePoliticsLaw

Abstract

fetched live from OpenAlex

In this essay, I consider three sets of narratives about youth in Toronto, in an ef-fort to think through the ways in which “youth,” both as concept and as persons, are configured and mobilized in the space of diaspora. I examine the nation-building burdens imposed upon youth occupying this terrain through my analysis of the criminalized representation of black youth in mass media; the culture-staging work of the Guyanese Social Club at York University; and a short video by Samuel Chow about his experience of migration from Hong Kong to Toronto and subsequent negotiations of racial and sexual identity in the city. I describe various racial, sexual, gendered and classed anxieties that represent youth as both threatening to and responsible for the reproduction of the nations they inhabit. This articulation, I suggest, simultaneously justifies the regulation of youth and reassures the logic of nationalism.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.219
Threshold uncertainty score0.440

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0460.017
Scholarly communication0.0080.005
Open science0.0020.011
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0060.000

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 head0.083
GPT teacher head0.360
Teacher spread0.277 · 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

Citations1
Published2007
Admission routes2
Has abstractyes

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