Bibliographic record
Abstract
Feng Min, Sun Lei, and Li Shangzhen initially established Ruhnn as a Taobao brand called "LiBeilin". With the rapid development and growing integration of social platforms and e-commerce functionality, they partnered with fashion model Zhang Dayi to open an exclusive online private store. Following the store's promising debut, LiBeilin officially renamed itself Ruhnn and adopted an "influencers + incubator + supply chain" operating model. With its self-operated business, Ruhnn looked to optimize its model as the supply chain was too complicated and focus on influencer incubation and operations. It also brought in business partners to pursue monetization. Ruhnn's FY2021Q1 results indicated a dramatic surge in profitability, with the platform business becoming a significant driver of robust growth. In recent years, influencer marketing has exploded with a host of multi-channel networks (MCNs) mushrooming across China. Ruhnn faced a barrage of criticism for relying on Weibo despite declining commercialization. Ruhnn's management team faced several issues. Could influencer incubation be replicated at scale through institutional operations? How should Ruhnn empower influencers and better serve businesses with influencer marketing needs? Could the company's organizational structure keep up with the expanding influencer pool and business size?
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; both teacher heads agree on what is shown here.
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".