Bibliographic record
Abstract
when meeting problems, different people may have different problem-solving styles. This phenomenon is easy to spot on campus. In the classroom, Chinese students often prefer to solve problems by themselves when they encounter problems, such as searching for answers on the Internet, and rarely discuss with professors or classmates. English-speaking students, on the other hand, prefer to solve problems in discussions, and they prefer to ask their own questions directly in class rather than finding answers after class. But in previous research, there has been little research on what leads to different problem-solving styles. Therefore, the following research will study the reasons why Chinese students and American students have different problem-solving styles from two aspects: historical reasons and educational systems. Chinese history has long been influenced by Confucianism, which emphasizes authoritarianism and collectivism, so Chinese students are more accustomed to receiving knowledge passively and solving problems passively. American history, on the other hand, emphasizes more free and logical thinking, so American students prefer to actively explore problems. In addition, China's unique college entrance exam education system allows Chinese students to do well in basic subjects, but the fill-in-the-blank education deprives Chinese students of the desire to actively explore knowledge. Compared to the Chinese education system, the U.S. education system was democratized after World War II, which has led to a diversity of educational resources, greater respect for students' interests, and enhanced student initiative. However, a liberal system is often more difficult to manage classes and students, and because there is no mandatory learning, American students generally perform worse in basic subjects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".