Happiness and satisfaction of foreign experts working in China and their influencing factors
Bibliographic record
Abstract
[eng] In this PhD thesis, I have analyzed the happiness, life satisfaction and work satisfaction of foreign scientists working in China, as well as which factors influence them. The main goal of my PhD thesis is to understand the degree and influencing factors of happiness and satisfaction of this collective of people, so that China can re-adjust policies to maximize it. I have found that around 19 statistically significant variables influence the three dependent variables (happiness, work satisfaction, and life satisfaction) in bivariate correlation. In order to deep into the multivariate statistical analysis, I have selected 11 variables by six groups into the model. The social demographic factors (gender, age, religion belief, education level, among others) don’t show statistically significant influence on any of the three dependent variables after controlling variables. However, the multi-statistic research demonstrates significant influence of variables from family background, economic division, life division, work division and social support division. Finally, I statistically found that the production of foreign talents working in China is not as significant as that of local Chinese scientists, which creates a dilemma for foreign talents working in China, as the working progress produce both satisfaction and dissatisfaction.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".