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Record W1965636202 · doi:10.1080/01425690802263643

Young people mobilizing the language of citizenship: struggles for classification and new meaning in an uncertain world

2008· article· en· W1965636202 on OpenAlexaboutno aff
Jacqueline Kennelly, Jo‐Anne Dillabough

Bibliographic record

VenueBritish Journal of Sociology of Education · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsnot available
FundersSpencer Foundation
KeywordsSociologyCitizenshipRetrenchmentGender studiesEthnographyMeaning (existential)LegitimacyPoliticsSocial scienceAnthropologyLinguisticsLawEpistemologyPolitical science

Abstract

fetched live from OpenAlex

This paper presents research findings from an ethnographic study carried out with 24 low‐income youths (ages 14–16) living on the economic fringes of urban inner‐city Vancouver, British Columbia, Canada. Our primary aims are: to expose the stratified subcultural articulations of citizenship as they are expressed, through language and symbol, by the young people within our study; and to demonstrate how critiques of (neo‐)liberalism in political thought, when combined with a cultural sociology of youth, might alter our subcultural reading of young people's conceptions of citizenship under the dynamics of radical social change. Our ultimate goal is to develop a more nuanced sociological examination of the ways in which young people deploy and utilize the language of citizenship as part of their own cultural struggles, exacerbated in times of state retrenchment, to classify themselves and others as one method of achieving visibility and legitimacy in urban concentrations of poverty.

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)0.004
metaresearch head score (Gemma)0.005
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 score0.174
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0140.018
Scholarly communication0.0110.003
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.086
GPT teacher head0.368
Teacher spread0.281 · 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

Citations71
Published2008
Admission routes1
Has abstractyes

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