Bibliographic record
Abstract
In 2019, Xibei sat firmly in the top tier of the Chinese catering industry, relying on its business model of creating shared value with all stakeholders. Jia Guolong, founder and chairman of Xibei, said that it was not because he or Xibei was noble, but due to commercial aspects. The value-sharing model aims to get greater returns from these shared values to build Xibei into a global giant. For the last 31 years, Xibei had developed a positive cycle of value creation with this model, and it grew more competitive. By the end of June 2019, Xibei had opened 350 stores in 56 cities across the country. In the future, Xibei aims to open its stores all over the world. Jia realized that it would be a great challenge to apply the value-sharing model in a larger market because stronger abilities to share and collect value were required in a larger market. After sharing business value or interest with more stakeholders, he hoped to receive more returns in terms of business value. But what was the best way to do it?
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".