Bibliographic record
Abstract
Driving by several internal and external challenges, Jiangsu Huabo Industrial Group Co., Ltd. (“Huabo Group”), a leading provincial distributor of mobile phones incorporated in 2001, have been exploring how to transform its offline distributing business into an online platform from 2013. In April 2014, Huabo Group incubated a new startup, Jiangsu PhoneWin Logistics Management Co., Ltd. (“PhoneWin”), by bringing together offline logistics services (PhoneWin Logistics) and an online platform (51dh.com.cn). Different with large established e-commerce platforms such as Taobao.com and JD.com focusing on individual customers in first- and second-tier cities at that time, PhoneWin was created to exploit opportunities to serve small stores in smaller towns and villages. The basic operation model is: When the small mobile phone stores in rural markets placed their orders on 51dh.com.cn, PhoneWin Logistics would deliver the phones before 4:00 p.m. the next day. By November 11, 2015, PhoneWin has expanded into 13 provinces across China, built partnerships with over 300 suppliers of mobile phones, and had over 80,000 small stores registered on its platform. In October 2015, it completed Series A funding of ￥120 million. However, two Chinese e-business giants, Taobao.com and JD.com, have started to expand their penetration in rural markets, which is becoming an inevitable threat to companies like PhoneWin. As an early entrant in this market, how can PhoneWin compete against such powerful giants? Will it be able to sustain its revenue and profit growth in the coming years?
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.164 | 0.043 |
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".