Research on English Situational Teaching in Primary Schools in China-Based on the Statistics and Analysis of CNKI Journals and Theses from 2014 to 2019
Bibliographic record
Abstract
Situational Teaching plays a crucial role in English classroom teaching in Chinese Primary Schools; hence the relevant research prospers simultaneously. This study is devoted to reveal the research characteristic on English Situational Teaching in Primary Schools in the past 6 years. Result of contrastive analysis and survey of the essays published on CNKI Journals and Theses from year 2014 to 2019 reveals: 1) In terms of the whole field, it develops steadily in spite of lack of wide coverage and depth. 2) In regarding to the research content in whole, it is relatively disproportionate, identical and superficial with too much perceptual thinking and the micro aspect of specific application and promotion but far less scientific, empirical and experimental research. Moreover, there are too many nonstandard essays and few outstanding ones. 3) As to the research method, the distribution of the employment of the 3 methods is imbalance with too little use of the experimental and quantitative ones. Furthermore, the journal authors are unskilled in employment of multiple methods. 4) With respect to the research team, it is unstable, low productive and too centralized with primary school teachers.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".