The Expansion of Higher Education and the Returns of Distance Education in China
Bibliographic record
Abstract
The returns of traditional face-to-face education are widely analyzed, but there is a need for empirical studies on the returns of distance education. Further, comparative studies on returns of both traditional and distance education using high-quality data are rare. Since 1999, continuous and rapid expansions have occurred in the whole Higher Education system in China. Given this background, what are the changes in returns of both traditional face-to-face education and distance education? This study analyzes the returns of both of these formats from 2003 to 2006 using the data from the China General Social Survey Open Database (Chinese General Social Survey [CGSS], 2018), adding educational background as a dummy variable to the Mincerian income equation. The empirical results show that Distance Higher Education can significantly increase the income of learners, the returns of distance education are lower than those of traditional face-to-face education and that from 2003 to 2006, the returns of distance education decrease dramatically.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".