Running Without Progress: How Game Theory Explains China’s GAOKAO Treadmill
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The intense competition in Chinese education has led to several problems in both family and society. To understand the process of how the intense competition appears, past researchers are showcasing the long history of Chinese Keju or using qualitative analysis to present the diverse influence of high involution. Here, I apply the quantitative analysis: Game Theory Model to give a novel lens to explain the situation. Particularly, I use signaling games for a macro perspective and prisoners' dilemma as the micro angle. To conclude, I find ways to break the intense competition by the analysis using Game theory. Overall, I suggest that the government should raise cost for extra studying to weaken the short-run incentive to betray; increase the discounted factor and reduce short term benefit differential to emphasize trust in cooperation; introduce holistic evaluation to deflate the signal so no students gains from overly investment and input.
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.
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 | 0.023 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it