Innovation of Ideological and Political Education Approaches in Universities under the Campus New Media Environment
Bibliographic record
Abstract
With the rapid development of internet technology, the application of new media in major universities is becoming increasingly widespread. New media not only provides excellent opportunities for the development of ideological and political education in universities but also poses challenges to the reform of ideological and political education. Ideological and political education in universities bears the important responsibility of cultivating students' quality, moral character, and ideological values. However, some ideological and political teachers in universities have not timely adjusted their teaching concepts and have insufficient grasp and utilization of the advantages of new media. The role of new media in assisting ideological and political teaching is not apparent. This article analyzes the problems of ideological and political teaching in the new media environment, explores the advantages of new media in teaching, and proposes effective paths for innovative ideological and political teaching. It is hoped that this will promote the development of ideological and political education in universities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".