Factors Affecting Job Opportunities for University Graduates in China---the Evidence from University Graduates in Beijing
Bibliographic record
Abstract
The problem of unemployed university graduates has become serious in China since the expansion of the higher education system in 1999, leading to an unemployment spell. Of China’s 5.6 million university graduates in 2008, 1.7 million are reported to have been unable to find jobs. In this paper, the factors that determine whether a graduate finds a job in China have been studied. A duration model for this study indicates that the graduates find jobs faster if they come from colleges with higher reputation. In addition, study shows graduates with engineering and business degrees find jobs more easily, next is major of arts and social science. The graduates with law and science degrees find jobs more difficult. Other majors have no significant effects on job finding. Finally, female graduates find jobs more easily than male graduates particularly before the final graduation date and 1-2 months after graduation.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".