Research on Chinese Audience's Perception of Online Fashion Week under the Influence of COVID-19
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to COVID-19, numerous offline events could not be hold as scheduled due to the restrictions of the quarantine of the pandemic, and this was also the case for the fashion industry. The 2022 Shanghai Fashion Week therefore opted for a completely online format, an unprecedented form innovation that is new to the industry. From augmented reality shows to meta-verse spaces, the fashion show uses digital technologies to express newest fashion to audiences. Although previous research has studied the audience reception of fashion weeks in China, few are tailored toward purely online fashion weeks. This research analyzes the attitudes of Chinese audiences towards online fashion weeks in the post-pandemic context. The research primarily uses surveys and interviews to obtain the necessary information, with secondary data from 2019 to 2022 collected over the internet. The study finds that on one hand, with its ease of access and with the influence of social media, online fashion week can have a larger exposure than offline. On the other hand, online shows are not a comprehensive presentation of clothes. Because viewers are not able to feel the clothes firsthand, the sales will be negatively affected. Therefore, the combination of "online + offline" fashion shows, having both the viral influence of online and the tangible feel of offline, may be the best of both worlds in the post-epidemic era.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.002 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it