A Major Difference between the Formation of English Words and the Formation of Chinese Words in Modern Times
Bibliographic record
Abstract
The English language is a language of “fertility” due to its continuous formation of new words in modern times. However, the Chinese language is “infertile” because it has basically stopped creating totally new words. The general trend in the development of a Chinese character in the Chinese history has been moving from complexity to simplicity. As a result, it leads to the "infertility" of the Chinese language and makes it difficult to combine a limited number of different strokes within a limited space known as方块字Fāngkuàizì ‘Square Block Word’. What is a totally new word in English is simply a combination of used words in Chinese. The Chinese language's capability of saving horizontal and linear space makes this combination feasible to express a new meaning. Three types of constraint arising from limited type and number of Strokes, General Trend toward Simplicity and Square-Framed Space have made their concurrent contribution to the "infertility" of the Chinese word formation. The preference of the Chinese language for new combinations of used words over the creation of total new Chinese words in modern times constitutes a major difference between the formation of English words and the formation of Chinese words in modern times.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".