Crafting alternative urban fashion infrastructure in a digital and pandemic age?
Bibliographic record
Abstract
INTRODUCTION Recent years have witnessed a “perfect storm” in the fashion industry. The rise of ultra-fast, online-only fashion (Camargo et al. 2020), the growing influence of platforms such as Amazon (Stewart 2022) and the ongoing pandemic (Brydges et al. 2021) have caused havoc for many fashion retailers. These developments have major implications for high streets and shopping malls in the world's fashion cities (i.e., centres with a concentration of leading fashion districts, designers and media, such as New York City, London, Milan and Shanghai). Recent media attention has focused on a number of major brands shuttering stores and filing for bankruptcy protection (Chitrakorn 2020). All of these challenges raise questions about the future of the fashion industry and its urban cultural infrastructure. This chapter focuses on one segment of the industry – small independent fashion retailers – examining their responses to the crisis. It explores the ways that these retailers forge alternative urban infrastructure and affective atmospheres, interweaving in-store sensory experiences with digital and social media technologies. Through a hybrid use of old factories and warehouses, independent fashion retailers engage different spaces and materials. They cultivate closer relationships between producers, consumers and designers, advancing a “politics of reconnection” (Hartwick 1998) that seeks to address the social and environmental costs of fashion. As the crisis in the fashion industry intensifies, these small urban retailers maintain diverse linkages with their surrounding neighbourhoods, and, during the pandemic, became key sites of economic and social resilience in the face of global supply chain disruptions. In the process, they foster alternative realms of fashion, illustrating how urban infrastructure is at once cultural and political, as well as material (Alam & Houston 2020). Utilizing a mixed methods approach, this chapter draws upon international trade reports, newspaper articles, websites and other social media, as well as open-ended interviews with alternative fashion retailers in Canadian and Australian cities. Organized into three main sections, it begins by providing an overview of global fashion infrastructure associated with major fashion centres. The second section discusses the crisis confronting the industry today and the third section examines how small, independent fashion retailers are responding to this crisis, utilizing physical, digital and cultural infrastructure to craft alternative urban fashion spaces.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".