An Empirical Study on the Performance of the American Government's Responsibility for Compulsory Education
Bibliographic record
Abstract
Over past years, the development of compulsory education in China has been featured with a regional imbalance, especially between urban and rural areas. The root causes of such an imbalance are multifold, mainly due to the unclear division of governmental responsibilities at all levels, the weak commitment of government responsibilities and mutual shirking. In comparison, owing to the advanced experiences of compulsory education, the three levels of government in the U.S. perform respective duty and own individual emphasis, thus forming a relatively stable and clear responsibility structure. Such a responsibility structure greatly promotes the development of compulsory education. Therefore, this paper mainly studies the status quo of both implementation and investment in compulsory education given by U.S. governments, irrespective of levels. Because it is of great inspiration for the Chinese counterparts to enhance its own social public power execution and duty fulfillment, in realm of compulsory education, crossing diverse levels.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".