Chinese Parents’ Effect on Children’s Math and Science Achievements in Schools with Different SES
Bibliographic record
Abstract
Chinese educational system is comprised of school, family and community, and coordination between school and family is the context for school management. Family education is the foundation of personal development with advantage in education time, content and techniques. This study chooses different school districts with different socio-cultural and economic backgrounds to compare and study the ways of parental involvement in different school districts and its impact on children’s STEM achievements in mathematics and science. In addition, the study also discussed the influence of parental involvement on children’s mathematics, physics, biology and geography in different family social economic status (SES) school districts. Generally speaking, parents’ expectations ranked first among the four areas, followed by parents’ communication, while school participation lies in the lowest. The lowest SES districts use the most parental control methods, and the highest SES area uses the most parental communication methods. The top two areas of SES use the most school communication methods. Furthermore, compared with girls, parents’ school participation has a significant negative impact on boys in the highest and lowest areas. Family communication has more influence on girls than boys. In school communication, it has the least impact on boys and girls.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".