The Practical Research on the Core Literacy of English Subject in the English Reading Class of Senior High School
Bibliographic record
Abstract
The discussion about the core literacy of high school students has quickly become one of the hot topics in the education circle. The core literacy of the English subject has promoted the reform process of English teaching in high schools in our country to a certain extent. However, due to many uncontrollable factors, such as the outdated English reading teaching model, and this model is difficult to be changed in a short time, there are still several problems in the current high school English reading classroom teaching design. Therefore, it is necessary to conduct research on this aspect. This article mainly uses research methods such as survey analysis, qualitative analysis, and quantitative analysis to conduct experiments. First, it studies the core literacy of the English subject, and second, analyzes the current situation of high school English classroom reading and the degree of core literacy used in the reading class. The results show that 85% of students spend less than 4 hours in English reading per week. In their daily study life, the time spent on English reading is very limited. 1% of high school students believe that the implementation of core literacy in English reading classrooms is generally effective. Therefore, it is particularly important to apply the core literacy of English subjects to high school English reading.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".