Research of Imitating the Thinking Orbit and Revolutionizing China’s English Education
Bibliographic record
Abstract
This essay has done research into some natural rules existing in the human brains ,especially the 0101…rule used as the principle of the computers and existing in the universe .The essay has discussed speech sound and written languge and their relationships with the purpose of choosing the correct orbit to imitae thinking.The essay has concluded that by means of imitating the thinking orbit we can bring about a revolution in studying and teaching English.Finally the essay sincerely suggests that the Chinese governments at all levels collect opinions and wisdom from many Chinese of ideals and integrity, follow the trend of the world languages, assimilate the essence of languages and reject the dross, and adopt the methods of imitating thinking to popularize English among the Chinese people by bringing the united efforts of the country into play. Key words: natural rules, imitate, thinking orbit, revolutionizing Resume: L’essai a recherche quelques regles naturelles existant dans les cerveaux, surtout la 0101…regle existant dans les ordinateurs, les cerveaux humains, et dans l’univers. L’essai s’est concentre sur trios orbites:l’ orbite sonore, l’orbite des caracteres ,et l’orbite du mode de pensee dans les cerveau. L’essai a conclu que etudier et enseigner l’anglais a l’aide de l’orbite de simulation, nous pouvons apporter une revolution dans l’enseignement de l’anglais et promouvoir considerablement la popularisation de l’anglais. Mots-Cles: regles naturelles, imiter, orbite pensante, revolutionner
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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 | 0.002 | 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 | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".