Study on Zhuang People’s Cognitive Situation From the Perspective of Sawndip’s Semantic Component: Based on Animals and Plants Sawndip
Bibliographic record
Abstract
Semantic component of a character is relevant to the meaning of a word which is recorded by the character. It is an important research idea for the modern cognitive science. Study shows that in terms of animals and plants Sawndip, Zhuang people learn the semantic component of Chinese characters systematically, and they are very clear about the categorical meaning of the semantic component. Sawndip has been created on the basis of Chinese characters, at the same time it has been reflecting fully the creativity of Zhuang people in the course of being used. On the one hand, Zhuang people have added the relative semantic component to some borrowing Chinese characters which did not have one. This phenomenon has shown that Zhuang people extremely emphasize the generic feature of things in the practice of cognition. On the other hand, to record some Zhuang words of animals and plants, Sawndip have the semantic components to be different from Chinese characters. Comparing with Han, this situation has shown that Zhuang people have different perceptions of the categories and characteristics of those animals and plants. The Zhuang people’s creative use of semantic component of Sawndip, it is an effective interpretation of “the Zhuang Localisation”.
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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 | 0.000 | 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 | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".