Bibliographic record
Abstract
51.com was founded as an online social network service provider in August 2005, and entered the online game industry when browser games were becoming popular in China. Although it has continually invested in developing online games, it has not broken into the ranks of the top 10 companies in China. There exist opportunities as well as challenges in the eyes of Pang Shengdong, the founder and CEO of 51.com. On the one hand, this industry has seen very fierce and complex competition in the past years. 51.com has been experiencing brain drain, a common problem in the online game industry. On the other hand, increasing numbers of profitable startups are emerging in this market, especially in the mobile game segment. To overcome the internal and external challenges, Pang decides to apply the spin-off approach within 51.com. This case aims to encourage the participants to discuss Pang’s efforts and the spin-off conducted within 51.com.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.078 | 0.018 |
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".