Bibliographic record
Abstract
这篇案例讲述了玫琳凯中国如何坚持“丰富女性人生”的全球使命以及四个黄金法则，并通过坚守直销模式兑现了其对美容顾问和员工的承诺。玫琳凯大中国区总裁麦予甫（Paul Mak）说道，对于玫琳凯，“P”和“L”不仅仅代表“利润（Profit）”和“亏损（Loss）”，更代表“人（People）”和“爱（Love）”。麦予甫深信，玫琳凯中国所坚持的企业文化是它得以生存和发展的基础，而玫琳凯文化在中国的落地和沉淀离不开人与人之间的信任。然而，随着企业规模不断扩大，新员工是否能够很快融入企业文化，人与人之间的网络是否有足够强的黏性，是否能够应对互联网带来的冲击？这是麦予甫对企业未来发展最大的担心。此外，他清楚，玫琳凯中国的业绩在很大程度上得益于其在全国三四线城市的渗透。但是，在全国性扩张的版图上还有很多空白，如何顺利拓展更多市场是目前他思索的第二个问题。最后，化妆品只是触及消费者的一个桥梁，但“美丽不止一面”，如何丰富产品线，不断地将有关“美丽”的产品提供给女性消费者呢？
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.020 |
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".