Bibliographic record
Abstract
There have been extensive research on Socratic teaching in various subject areas in China, most of which are about the application of Socratic method in teaching and its enlightenment to the teaching classroom. Many authors have studied Socratic teaching through experiments and achieved good results. This paper reviews the theoretical and practical feasibility of Socratic classroom teaching in different fields. This paper introduces several common teaching methods, such as the lecturing teaching method, inquiry teaching method, practice teaching method and example teaching method. There are some similarities and differences between Socratic teaching method and these common teaching methods. Compared with these common teaching methods, the Socratic teaching method is an innovation in teaching. Some theoretical and experimental arguments can be obtained from the literature to prove that Socratic teaching is feasible in both theory and practice. Socratic teaching is of great significance to the education of most scholars, who can get some experience from it. Through the experience of Socratic teaching mode in the classroom, this study hopes to inspire teachers to be student-centered and pay attention to the guidance of students in teaching rather than indoctrination.
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 | 0.037 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".