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Record W2978936627 · doi:10.1386/ajpc_00008_1

Reconciling the local and the global in the Brisbane independent fashion sector

2019· article· en· W2978936627 on OpenAlexaboutno aff
Alexandra Tuite

Bibliographic record

VenueAustralasian Journal of Popular Culture · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic geographyGlobal cityFashion industryGeographySociologyEconomyRegional sciencePolitical scienceArchaeologyEconomicsClothing

Abstract

fetched live from OpenAlex

The humid, sub-tropical city of Brisbane is Australia’s third-largest and one often regarded as culturally inferior to its southern counterparts Sydney and Melbourne. However, the city has supported a small but active independent fashion scene, and this article examines three of these businesses, exploring how they positioned themselves in relation to the global fashion industry. It contributes to literature on local fashion industries in New Zealand, Scandinavia, Canada and the United Kingdom. Challenges and opportunities presented to local fashion businesses are considered, and ways in which these have changed over time is also discussed. Case studies are drawn from a period between 1950 and 2018 and were purposively chosen so that contemporary case studies could be contextualized with historical examples. Research was conducted through archival research at the Queensland Museum, semi-structured interviews with participants and on-site observations. Findings confirm those of existing studies in the field that suggest local fashion businesses outside of large cities and dominant fashion centres may struggle to remain relevant in a fast-paced global industry, but have an opportunity to develop and foster close bonds to local cultural scenes and to contribute to place-making in the cities in which they are located.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.294

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.278
Teacher spread0.253 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2019
Admission routes1
Has abstractyes

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