Bibliographic record
Abstract
This major research project proposes that nostalgia centrally applies to the COVID-19 pandemic in that the pandemic has profoundly impacted how consumers dress and shop. With the COVID-19 pandemic causing lockdowns, quarantines, and isolation, the resulting rise of mental health issues went hand in hand with changes in fashion marketing and consumer shopping. This major research project offers an analysis of two case studies providing insight into how fashion brands invoke feelings of nostalgia in consumers by creating items or collections that recall memories of the past including the use of iconic visual images recalling childhood-themed popular culture products from the recent past. The first case study focuses on Miu Miu's Spring 2022 collection and the resurgence of Y2K style while the second analyzes LOEWE's 2021 My Neighbor Totoro collection, 2022 Spirited Away collection, and 2023 Howl's Moving Castle collection in collaboration with Japanese animation company Studio Ghibli. Overall, this study contributes to the intersecting fields of Fashion Studies, COVID-19 Studies, and Affect studies by examining nostalgia's relationship with global disruption. The study argues that evidence of nostalgia-themed fashion foci during COVID-19 appealed to consumers trying to cope with, and escape from, the stresses and strains of the pandemic.
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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 | 0.002 |
| 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 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".