The Constitutional Monarchy and Modernization: Kang Youwei's Perspectives on "Keeping the Emperor and the Royal System in China"
Bibliographic record
Abstract
At the end of the 19th century, China opened up its age of democratic revolution. Kang Youwei, a senior intellectual, advised Emperor Guangxu to establish the Constitutional Monarchy system in China as England and Japan did. However, his thoughts were condemned as “conservative”, “anti-revolutionary” by those radicals in his time. In this article, the author makes a deep exploration of the Constitutional Monarchy system in different countries in today’s world, the social roles of kings and monarchs, and the economic positions of today’s Constitutional Monarchy countries in the world. It is found that nearly all these countries are developed countries which gained political stability and economic prosperity by establishing the constitutional monarchy system and account for a large percentage in the world’s most developed countries. Compared to those democratic republican countries of the same period, they developed in a more stable and rapid way. The kings and queens play important roles in keeping the country stable and prosperous. Thus, history has proven that the Constitutional Monarchy is a great democratic system and Kang Youwei’s proposal of “keeping the emperor and the royal system” and establishing the Constitutional Monarchy system was the most suitable choice in terms of the situation of China at his time.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".