Interdisciplinary Education of Foreign Language Majors in Chinese Local Universities under the Background of New Liberal Arts
Bibliographic record
Abstract
New liberal arts refer to the reorganization of traditional liberal arts to realize the intersection and integration within liberal arts and between liberal arts and natural sciences. The characteristics of new liberal arts are mainly problem-orientation, cross-integration, new technology application and innovative development. Under the background of new liberal arts, the implementation of interdisciplinary education in foreign language majors is an effective way for local colleges and universities to promote the construction of "new foreign languages" and the training of interdisciplinary and applied foreign language talents. Based on the connotations of new liberal arts and interdisciplinary education, and in view of the institutional and cultural barriers to the planning and implementation of interdisciplinary education, this paper proposes an optimized path for the design and implementation of interdisciplinary education for foreign language majors in local colleges and universities: deepen the integration between foreign language majors and other disciplines, build a staged, multi-level talent training system, set up cross-discipline curricular groups, apply new technology to transform traditional teaching and learning methods, strengthen the construction of interdisciplinary education platforms and faculties, and establish a foreign language interdisciplinary education system guarantee mechanism.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".