A Review of Interpreting Teaching Research in China Based on CiteSpace (2008-2018)
Bibliographic record
Abstract
After thirty years of development, the teaching of interpreting in China has achieved a moderate scale giving the credit to the growing demand for interpreting talents, as well as the spread of interpreting teaching among universities. This study reviewed the papers on interpreting teaching research published for the past decade in China covered by Chinese Social Sciences Citation Index (CSSCI) of foreign languages and education studies, explored the quantity of publications, source journals, authors, institutions and research hotspots by CiteSpace, depicted the mapping knowledge domain and analyzed the problems of the current research. Through bibliometric analysis, it is found that (1) High-level interpreting teaching research papers are produced in limited quantities; (2) Interpreting teaching research in China has not yet formed an independent research field; (3) The traits of interpreting discipline are not obvious enough; (4) The cross-sectional research and the longitudinal study have not been widely concerned; (5) The research on the backwash effect of interpreting tests are few; (6) The research on interpreting teaching at universities of applied sciences are limited. Based on the research findings, reference for further study of interpreting teaching would be provided.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.033 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 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".