A Comparative Study of Educational Opportunities for Disadvantaged Children in China and The United States--Taking Disadvantaged Children of Ethnic Minorities in The Two Countries as An Example
Bibliographic record
Abstract
The issue of educational equity is a hot topic of concern to the whole society. Some researchers have found that there is a lack of educational opportunities for ethnic minority groups in China and the United States, but there is still a lack of unified explanation for the causes, current situation, and the advantages and disadvantages of solutions to the problem of educational equity for ethnic minorities in both countries. Therefore, this article conducts research on the education situation of ethnic minorities in both countries by collecting relevant data and summarizing and analyzing the data. Research has found significant differences in the causes and current situation of ethnic minority education issues between China and the United States, and there is also room for improvement in existing solutions. Therefore, China and the United States need to develop precise measures that are suitable for their own specific situations based on their respective special circumstances to address the issue of educational equity faced by ethnic minorities.
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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 | 0.003 | 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 | 0.001 | 0.002 |
| 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".