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Record W2004003124 · doi:10.1080/13504630.2011.570972

Shopping for identity: articulations of gender, race and class by critical consumers

2011· article· en· W2004003124 on OpenAlexaffabout
Kaela Jubas

Bibliographic record

VenueSocial Identities · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Ethnicity, and Economy
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSociologyGender studiesRace (biology)MetaphorContext (archaeology)Identity (music)HegemonyClass (philosophy)MulticulturalismConsumption (sociology)CitizenshipNetnographyGlobalizationSocial sciencePolitical sciencePoliticsEpistemologyAestheticsSocial mediaLawLinguisticsPedagogy

Abstract

fetched live from OpenAlex

This article discusses a study which explored shopping as a process of incidental adult learning about consumption, globalization and citizenship among self-identified critical shoppers in Vancouver, Canada. The author focuses on participants' comments about social identity, especially in terms of gender, race and class. Reflecting current concerns, many participants noted that the environment and (un)fair trade influenced their shopping practices, and helped them understand themselves in the context of a ‘multicultural’ society and a ‘globalized’ world. This article borrows from the jargon of municipal recycling programs, part of a critical consumption discourse, in outlining how participants' comments seem to ‘reduce,’ ‘reuse,’ and/or ‘recycle’ hegemonic notions of gender, race and class. Working from a neo-Gramscian perspective, the author uses this metaphor to explore both the tendency to reiterate an understanding of gender, race and class as essentialized characteristics and attempt to resist that simplistic understanding.

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)0.008
metaresearch head score (Gemma)0.010
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 score0.117
Threshold uncertainty score0.232

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0240.039
Scholarly communication0.0110.007
Open science0.0010.006
Research integrity0.0020.003
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 head0.100
GPT teacher head0.360
Teacher spread0.259 · 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

Citations7
Published2011
Admission routes2
Has abstractyes

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