Bibliographic record
Abstract
Before the double reduction policy, parents tended to arrange after-school classes for a primary school student; yet, some of these institutions were not as good as they pronounced.Thus, parents were anxious when their children had not made any progress.On the other hand, as most parents at work could not go back home before students' time of after class from Monday to Friday, they had to arrange after-school classes for their children and paid more money for the after-school institutions.After the implementation of the double reduction policy, teachers need to help the students with their homework and solve the learning problems.It aims to help the students finish most of the work at school and no need to take it back home or ask the teachers in the institutions to help them.Therefore, a double reduction policy is of great necessity.This paper reviews practice from Quebec, a qualitative study by Carol, and evaluation in over two hundred New Zealand schools.The researcher also reflects on these practices and raises some suggestions on how to build a harmonious relationship between teachers and parents under the implementation of a double reduction policy.Through reviewing and summarizing the historical practice and the latest policy of China education policy, the researcher clarifies from these four perspectives: (1) The characteristics of primary school parents in China; (2) Law on parents instruction, curriculum reform, and publications of documents; (3) Design interactive homework assignments; (4) Teacher quality and competence.
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.
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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".