Bibliographic record
Abstract
Written by Xu Hui and Mint Kang and translated by Muhammad Firdaus Ariff, this book is about an ancient Chinese character of Tao Zhugong. In every corner of the globe, there's a Chinatown. Chinese entrepreneurs have built a solid financial base almost anywhere they are. They become a new driving force in the global economic network. Chinese people to leave their homes to migrate to foreign lands and had a hard life. Some small businesses make when they have little savings. They overcome the language barrier and adapt to the new way of life and move their business based on the code of ethics and practices of traditional business. Thanks to the determination, they succeeded. Almost every Chinese businessman has their own code of practice for the family business. There were revealed word of mouth from father to son. There are recorded as ingenious formula, framed and hung on the wall. This family business code exactly the same, there may be a longer and more detailed. But all of them can be traced back to Tao Zhugong. In China, Tao Zhugong name is synonymous with a very rich man. Therefore there is a saying 'rich like Tao Zhugong'. But Tao Zhugong is not highly regarded on his richness alone.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.009 |
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".