2021 Global Branding Strategy & Fashion Industry White Paper:Sustainable Fashion and New Consumption in a Global Strategic Perspective
Bibliographic record
Abstract
The research fund conducted surveys to learn about consumers’ sustainable consumption habits and ideas, and sound out public opinions on hot topics in new consumption trends such as experience economy, niche hobbies, and virtual idols. It also worked closely with brands, companies, and platforms on sustainable fashion. The research fund collaborated with the Shanghai Fashion Week Organizing Committee to hold the Symposium on Sustainable Fashion which was attended by business and government representatives and developed cases about sustainable fashion in partnership with Kering, Kane Top, and Melephant. The first CEIBS-Kering Acceleration Camp for Sustainable Innovation was also launched through the joint efforts of CEIBS and Kering. To look into young consumers and new consumption trends, the research fund partnered with Shui On Xintiandi to hold the forum Fashion Innovation Under the Rise of Young Chinese Consumers, and delved deeper into the virtual beings industry through follow-on research and interview. Correspondingly, this white paper covers three main topics: consumer research report on the Chinese market, sustainable fashion, and new consumption. It contains one report on consumer surveys completed by the research fund over the past year, three CEIBS teaching cases, two forums held by the fund in partnership with the Shanghai Fashion Week Organizing Committee and Shui On Xintiandi, and recaps of relevant events such as the CEIBS-Kering Acceleration Camp for Sustainable Innovation.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.085 | 0.065 |
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".