Research on Chang Po-Ling’s Practice of Persuading People to Donate Money on Education and Its Practical Significance
Bibliographic record
Abstract
As a well-known founder of the Nankai School in the Republican period of China, Chang Po-ling started an example to develop private education and persuade people to donate money on education. During this process, due to his “Nankai spirit”of diligence and striving, Mr. Chang Po-ling who was full of strong patriotic sentiment from beginning to end, affected a lot of domestic military and political figures, entrepreneurs and business owners, schoolfellows, directors, international friends, founds and organizations, and attracted them to support and focus on education initiators. Through various ways and a series of practical activities, Chang Po-ling persistently encouraged everyone to donate money on education and gained the continuous development of Nankai School. When looking back at Chang Po-ling’s process of persuading people to donate money on education, we can not only see his extremely excellent and brilliant achievements on school running, but also can study and reflect on how to utilize social force to service for current education.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".