Bibliographic record
Abstract
Zhongyong is a moral standard set forth by Confucian culture, meaning maintaining neutrality and moderation. Many people tend to have a misunderstanding of Zhongyong thinking, believing that Zhongyong represents compromise and the middle position. They might consider that such thinking is detrimental to healthy mental development. However, Zhongyong thinking is not the same as compromise, and instead it allows the coexistence of contradictions and opposites. After reviewing a series of previous articles on Zhongyong thinking and mental health, it is shown that Zhongyong thinking plays a moderating role in the employees’ psychological state. Moreover, the factors affecting the influence of Zhongyong thinking on employees' mental health include psychological resilience and psychological security. Ultimately, the review found that one of the main reasons for the moderation of psychological states in the workplace is because it affects emotional regulation and alleviates symptoms of depression and anxiety. Plus, Zhongyong thinking related training also was shown some benefits. A limitation of previous studies is that subjective questionnaires were used to measure the level of Zhongyong thinking and other psychological variables, which could be less accurate. Future research directions could explore how to accurately demonstrate dynamic Zhongyong thinking processes through experimental and longitudinal design.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".