Bibliographic record
Abstract
Chinese is one of the oldest and most widely used languages with an ideographic writing system. It is curious to analyze the advantages and disadvantages of Chinese in cognitive linguistics, cognitive informatics, and knowledge science. This paper presents a comparative study on the fundamental theories and formal models of Chinese and other languages. A number of interesting findings on the cognitive and social impacts of the widely used Chinese language are revealed. It is found that, although the idiographic languages are more efficient in language manipulation, the alphabetic languages contributed more to the development of the knowledge processing power of the brain. A set of fundamental properties of knowledge is elicited, which reveals that the knowledge space of an individual is proportional to both the number of concepts and the number of their relations developed in long-term memory of the brain. Toward a more powerful and efficient scientific language for rigorous inference, the expression means of the Chinese language may yet need to be extended in its abstraction mechanisms and a convergent approach to integrate and synergize observations and truths in order to form rigorous theories and a formal knowledge framework. The findings of this work provide a foundation for comparative studies on Chinese and other languages in particular, and for cognitive linguistics and knowledge science in general.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".