Diverse Education Based on Specific Conditions in Rural Areas of China
Bibliographic record
Abstract
Connecting the spiritual outlook of the future society, education is the cornerstone of the rejuvenation of a nation. By the year 2018, nearly 600 million people lived in rural areas of China. For being such a large group, their basic necessities of life, and each and every move are closely related to the country’s destiny. So when these two parts get associated, the problem become larger and more difficult. Nowadays we should view the rural education with new eyes. Affected by the deepening of the market economy, the expansion of higher education and the tough job market, rural residents’ views on education are changing and can gradually be divided into two categories: the one is that education is the steering wheel which can lead to the change of fate; the other one is that the education is no longer the only way out. The cost of continuing a child’s education must be seen first. This article holds that in the process of revitalizing rural education, we should take measures according to local conditions basing on rural characteristics, and attach importance to the development of a multi-level and diverse education so that students can see more possibilities besides study and work, which will not only benefit the development of individuals, but also contribute to the progress of the whole society.
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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 | 0.000 | 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 | 0.000 |
| 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".